Ab Initio Approach for Thermodynamic Surface Phases with Full Consideration of Anharmonic Effects: The Example of Hydrogen at Si(100) - Zhou, Yuanyuan; Zhu, Chunye; Scheffler, Matthias; Ghiringhelli, Luca M. (Physical Review Letters, 2022)
A reliable description of surfaces structures in a reactive environment is crucial to understand materials' functions. We present a first-principles theory of replica-exchange grand-canonical-ensemble molecular dynamics and apply it to evaluate phase equilibria of surfaces in a reactive gas-phase environment. We identify the different surface phases and locate phase boundaries including triple and critical points. The approach is demonstrated by addressing open questions for the Si(100) surface in contact with a hydrogen atmosphere. In the range from 300 to 1000 K, we find 25 distinct thermodynamically stable surface phases, for which we also provide microscopic descriptions. Most of the identified phases, including few order-disorder phase transitions, have not yet been observed experimentally. Furthermore, we show that the dynamic Si-Si bonds forming and breaking is the driving force behind the phase transition between 3×1 and 2×1 adsorption patterns.
CRIS-Team Scopus Importer:2023-09-25

Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence - Purcell, Thomas A.R.; Scheffler, Matthias; Ghiringhelli, Luca M.; Carbogno, Christian (npj Computational Materials, 2023)
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.
CRIS-Team Scopus Importer:2023-09-25

An AI-toolkit to develop and share research into new materials - Ghiringhelli, Luca M. (Nature Reviews Physics, 2021)
CRIS-Team Scopus Importer:2023-09-25

Analytic Approach and Monte Carlo Sampling for Electron Correlations - Ghiringhelli, Luca M.; Site, Luigi Delle (Recent Progress in Orbital-free Density Functional Theory, 2013)
By combining rigorous mathematical conditions and a computational approach for sampling the electronic configurations in space, we build a procedure for the design of kinetic functionals for electrons which is internally consistent. We then apply it to the test case of the almost uniform interacting electron gas and derive the corresponding kinetic functional. Interestingly the functional form obtained has strong similarities with the Shannon Entropy within the Theory of Information.
CRIS-Team Scopus Importer:2023-09-25

Application of (Kohn–Sham) Density-Functional Theory to Real Materials - Ghiringhelli, Luca M. (Many-Electron Approaches in Physics, Chemistry and Mathematics, 2014)
Hohenberg and Kohn proved the existence and uniqueness of a functional of the electron density, whose minimization yields the ground-state density n(r) of a bound system of N interacting electrons in some external potential v(r). The exact expression of the universal density functional is however elusive. In this chapter, I describe the several attempts made for designing an approximation to the density functional that gave accurate results for “real materials” (molecules, clusters, and extended materials). All discussed approximations originate from the Kohn–Sham approach, a particular (but almost universally adopted) formulation of the density-functional theory, in which the variational problem of the N interacting electrons is recast into a set of N one-particle equations where each electron acts in the mean field generated by all the other electrons.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Generalize Gradient Approximation; Generalize Gradient Approximation Approximation; Generalize Gradient Approximation Functional; Mean Absolute Relative Error; Potential Energy Surface

A quantum reactive scattering perspective on electronic nonadiabaticity - Peng, Yang; Ghiringhelli, Luca M.; Appel, Heiko (European Physical Journal B, 2014)
Based on quantum reactive-scattering theory, we propose a method for studying the electronic nonadiabaticity in collision processes involving electron-ion rearrangements. We investigate the state-to-state transition probability for electron-ion rearrangements with two comparable approaches. In the first approach the information of the electron is only contained in the ground-state Born-Oppenheimer potential-energy surface, which is the starting point of common reactive-scattering calculations. In the second approach, the electron is explicitly taken into account and included in the calculations at the same level as the ions. Hence, the deviation in the results between the two approaches directly reflects the electronic nonadiabaticity during the collision process. To illustrate the method, we apply it to the well-known proton-transfer model of Shin and Metiu, generalized in order to allow for reactive scattering channels. We show that our explicit electron approach is able to capture electronic nonadiabaticity and the renormalization of the reaction barrier near the classical turning points of the potential in nuclear configuration space. In contrast, system properties near the equilibrium geometry of the asymptotic scattering channels are hardly affected by electronic nonadiabatic effects. We also present an analytical expression for the transition amplitude of the asymmetric proton-transfer model based on the direct evaluation of integrals over the involved Airy functions.
CRIS-Team Scopus Importer:2023-09-25

Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides - Mazheika, Aliaksei; Wang, Yang Gang; Valero, Rosendo; Viñes, Francesc; Illas, Francesc; Ghiringhelli, Luca M.; Levchenko, Sergey V.; Scheffler, Matthias (Nature Communications, 2022)
Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion.
CRIS-Team Scopus Importer:2023-09-25

Artificial intelligence for high-throughput discovery of topological insulators: The example of alloyed tetradymites - Cao, Guohua; Ouyang, Runhai; Ghiringhelli, Luca M.; Scheffler, Matthias; Liu, Huijun; Carbogno, Christian; Zhang, Zhenyu (Physical Review Materials, 2020)
Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a two-dimensional descriptor by applying an artificial intelligence (AI)-based approach for fast and reliable identification of the topological characters of a drastically expanded range of materials, without prior determination of their specific symmetries and detailed band structures. By leveraging this descriptor that contains only the atomic number and electronegativity of the constituent species, we have readily scanned a huge number of alloys in the tetradymite family. Strikingly, nearly half of them are identified to be topological insulators, revealing a much larger territory of the topological materials world. The present work also attests to the increasingly important role of such AI-based approaches in modern materials discovery.
CRIS-Team Scopus Importer:2023-09-25

Autocatalytic and cooperatively stabilized dissociation of water on a stepped platinum surface - Donadio, Davide; Ghiringhelli, Luca M.; Delle Site, Luigi (Journal of the American Chemical Society, 2012)
Water-metal interfaces are ubiquitous and play a key role in many chemical processes, from catalysis to corrosion. Whereas water adlayers on atomically flat transition metal surfaces have been investigated in depth, little is known about the chemistry of water on stepped surfaces, commonly occurring in realistic situations. Using first-principles simulations, we study the adsorption of water on a stepped platinum surface. We find that water adsorbs preferentially at the step edge, forming linear clusters or chains, stabilized by the cooperative effect of chemical bonds with the substrate and hydrogen bonds. In contrast with their behavior on flat Pt, at steps water molecules dissociate, forming mixed hydroxyl/water structures, through an autocatalytic mechanism promoted by H-bonding. Nuclear quantum effects contribute to stabilize partially dissociated cluster and chains. Together with the recently demonstrated behavior of water chains adsorbed on stepped Pt surfaces to transfer protons via thermally activated hopping, these findings make these systems viable candidates for proton wires. © 2012 American Chemical Society.
CRIS-Team Scopus Importer:2023-09-25

Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy - Leitherer, Andreas; Yeo, Byung Chul; Liebscher, Christian H.; Ghiringhelli, Luca M. (npj Computational Materials, 2023)
Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy (STEM) images of polycrystalline materials. The method is based on a Bayesian convolutional neural network (BNN) that is trained only on simulated images. AI-STEM automatically and accurately identifies crystal structure, lattice orientation, and location of interface regions in synthetic and experimental images. The model is trained on cubic and hexagonal crystal structures, yielding classifications and uncertainty estimates, while no explicit information on structural patterns at the interfaces is included during training. This work combines principles from probabilistic modeling, deep learning, and information theory, enabling automatic analysis of experimental, atomic-resolution images.
CRIS-Team Scopus Importer:2023-10-20

Big data of materials science: Critical role of the descriptor - Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias (Physical Review Letters, 2015)
Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.
CRIS-Team Scopus Importer:2023-09-25

Computational design of nanoclusters by property-based genetic algorithms: Tuning the electronic properties of (TiO2)n clusters - Bhattacharya, Saswata; Sonin, Benjamin H.; Jumonville, Christopher J.; Ghiringhelli, Luca M.; Marom, Noa (Physical Review B - Condensed Matter and Materials Physics, 2015)
In order to design clusters with desired properties, we have implemented a suite of genetic algorithms tailored to optimize for low total energy, high vertical electron affinity (VEA), and low vertical ionization potential (VIP). Applied to (TiO2)n clusters, the property-based optimization reveals the underlying structure-property relations and the structural features that may serve as active sites for catalysis. High VEA and low VIP are correlated with the presence of several dangling-O atoms and their proximity, respectively. We show that the electronic properties of (TiO2)n up to n=20 correlate more strongly with the presence of these structural features than with size.
CRIS-Team Scopus Importer:2023-09-25

Correction to: Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery (Topics in Catalysis, (2022), 65, 1-4, (196-206), 10.1007/s11244-021-01502-4) - Foppa, Lucas; Ghiringhelli, Luca M. (Topics in Catalysis, 2022)
The original version of this article unfortunately contained an error and it is corrected with this erratum.
CRIS-Team Scopus Importer:2023-09-25

Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition - Sutton, Christopher; Ghiringhelli, Luca M.; Yamamoto, Takenori; Lysogorskiy, Yury; Blumenthal, Lars; Hammerschmidt, Thomas; Golebiowski, Jacek R.; Liu, Xiangyue; Ziletti, Angelo; Scheffler, Matthias (npj Computational Materials, 2019)
A public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (AlxGayIn1–x–y)2O3 compounds. Its aim was to identify the best machine-learning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy.
CRIS-Team Scopus Importer:2023-09-25

Data-driven equation for drug-membrane permeability across drugs and membranes - Dutta, Arghya; Vreeken, Jilles; Ghiringhelli, Luca M.; Bereau, Tristan (Journal of Chemical Physics, 2021)
Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 106 small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.
CRIS-Team Scopus Importer:2023-09-25

Determining surface phase diagrams including anharmonic effects - Zhou, Yuanyuan; Scheffler, Matthias; Ghiringhelli, Luca M. (Physical Review B, 2019)
We introduce a massively parallel replica-exchange grand-canonical sampling algorithm to simulate materials at realistic conditions, in particular surfaces and clusters in reactive atmospheres. Its purpose is to determine in an automated fashion equilibrium phase diagrams for a given potential-energy surface and for any observable sampled in the grand-canonical ensemble. The approach enables an unbiased sampling of the phase space and is embarrassingly parallel. It is demonstrated for a model of the Lennard-Jones system describing a surface in contact with a gas phase. Furthermore, the algorithm is applied to SiM clusters (M=2,4) in contact with an H2 atmosphere, with all interactions described at the ab initio level, i.e., via density-functional theory, with the Perdew-Burke-Ernzerhof gradient-corrected exchange-correlation functional. We identify the most thermodynamically stable phases at finite T,p(H2) conditions.
CRIS-Team Scopus Importer:2023-09-25

Discovering dynamic laws from observations: The case of self-propelled, interacting colloids - Ruiz-Garcia, Miguel; Barriuso, C. Miguel G.; Alexander, Lachlan C.; Aarts, Dirk G.A.L.; Ghiringhelli, Luca M.; Valeriani, Chantal (Physical Review E, 2024)
Active matter spans a wide range of time and length scales, from groups of cells and synthetic self-propelled colloids to schools of fish and flocks of birds. The theoretical framework describing these systems has shown tremendous success in finding universal phenomenology. However, further progress is often burdened by the difficulty of determining forces controlling the dynamics of individual elements within each system. Accessing this local information is pivotal for the understanding of the physics governing an ensemble of active particles and for the creation of numerical models capable of explaining the observed collective phenomena. In this work, we present ActiveNet, a machine-learning tool consisting of a graph neural network that uses the collective motion of particles to learn active and two-body forces controlling their individual dynamics. We verify our approach using numerical simulations of active Brownian particles, active particles undergoing underdamped Langevin dynamics, and chiral active Brownian particles considering different interaction potentials and values of activity. Interestingly, ActiveNet can equally learn conservative or nonconservative forces as well as torques. Moreover, ActiveNet has proven to be a useful tool to learn the stochastic contribution to the forces, enabling the estimation of the diffusion coefficients. Therefore, all coefficients of the equation of motion of Active Brownian Particles are captured. Finally, we apply ActiveNet to experiments of electrophoretic Janus particles, extracting the active and two-body forces controlling colloids' dynamics. On the one side, we have learned that the active force depends on the electric field and area fraction. On the other side, we have also discovered a dependence of the two-body interaction with the electric field that leads us to propose that the dominant force between active colloids is a screened electrostatic interaction with a constant length scale. We believe that the proposed methodological tool, ActiveNet, might open a new avenue for the study and modeling of experimental suspensions of active particles.
CRIS-Team Scopus Importer:2024-07-19

Efficient ab initio schemes for finding thermodynamically stable and metastable atomic structures: Benchmark of cascade genetic algorithms - Bhattacharya, Saswata; Levchenko, Sergey V.; Ghiringhelli, Luca M.; Scheffler, Matthias (New Journal of Physics, 2014)
A first principles-based methodology for efficiently and accurately finding thermodynamically stable and metastable atomic structures is introduced and benchmarked. The approach is demonstrated for gas-phase metal-oxide clusters in thermodynamic equilibrium with a reactive (oxygen) atmosphere at finite pressure and temperature. It consists of two steps. First, the potential-energy surface is scanned by means of a global-optimization technique, i.e., a massive-parallel first-principles cascade genetic algorithm for which the choice of all parameters is validated against higher-level methods. In particular, we validate (a) the criteria for selection and combination of structures used for the assemblage of new candidate structures, and (b) the choice of the exchange-correlation functional. The selection criteria are validated against a fully unbiased method: replica-exchange molecular dynamics. Our choice of exchange-correlation functional, the van der Waals-corrected PBE0 hybrid functional, is justified by comparisons up to the highest level currently achievable within density-functional theory, i.e., the renormalized second-order perturbation theory. In the second step, the low-energy structures are analyzed by means of ab initio atomistic thermodynamics in order to determine compositions and structures that minimize the Gibbs free energy at given temperature and pressure of the reactive atmosphere.
CRIS-Team Scopus Importer:2023-09-25
Keywords: atomistic thermodynamics; cascade genetic algorithm; clusters; density functional theory; MgO; reactive environment

Electronic energy functionals: Levy-Lieb principle within the ground state path integral quantum Monte Carlo - Delle Site, Luigi; Ghiringhelli, Luca M.; Ceperley, David M. (International Journal of Quantum Chemistry, 2013)
We propose a theoretical/computational protocol based on the use of the Ground State Path Integral Quantum Monte Carlo for the calculation of the kinetic and Coulomb energy density for a system of N interacting electrons in an external potential. The idea is based on the derivation of the energy densities via the (N - 1)-conditional probability density within the framework of the Levy-Lieb constrained search principle. The consequences for the development of energy functionals within the context of density functional theory are discussed. We propose also the possibility of going beyond the energy densities and extend this idea to a computational procedure where the (N - 1)-conditional probability is an implicit functional of the electron density, independently from the external potential. In principle, such a procedure paves the way for an on-the-fly determination of the energy functional for any system. © 2012 Wiley Periodicals, Inc.
CRIS-Team Scopus Importer:2023-09-25
Keywords: advanced Monte Carlo; density functional theory; ground state path integral quantum Monte Carlo; kinetic energy functional; Levy-Lieb constrained search

Erratum: "Intense fluorescence of Au20" [J. Chem. Phys. 147, 074301 (2017) - Yu, Chongqi; Harbich, Wolfgang; Sementa, Luca; Ghiringhelli, Luca M.; Aprá, Edoardo; Stener, Mauro; Fortunelli, Alessandro; Brune, Harald (Journal of Chemical Physics, 2017)
CRIS-Team Scopus Importer:2023-09-25

Extrapolation to the complete basis-set limit in density-functional theory using statistical learning - Speckhard, Daniel T.; Carbogno, Christian; Ghiringhelli, Luca M.; Lubeck, Sven; Scheffler, Matthias; Draxl, Claudia (Physical Review Materials, 2025)
The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit for periodic crystal structures. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model and a symbolic regression approach using the SISSO model are used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. SISSO outperforms the random forest model for the exciting code. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions.
CRIS-Team Scopus Importer:2025-01-31

FAIR Research Data With NOMAD FAIRmat's Distributed, Schema-based Research-data Infrastructure to Harmonize RDM in Materials Science - Scheidgen, Markus; Brückner, Sebastian; Brockhauser, Sandor; Ghiringhelli, Luca M.; Dietrich, Felix; Mansour, Ahmed E.; Marquez, Jose A.; Albrecht, Martin; Weber, Heiko B.; Botti, Silvana; Aeschlimann, Martin; Draxl, Claudia (1st Conference on Research Data Infrastructure (CoRDI), 2023)
Scientific research is becoming increasingly data centric, which requires more effort to manage, share, and publish data. NOMAD is a web-based platform that provides research data management (RDM) for materials-science data. In addition to core RDM functions like uploading and sharing files, NOMAD automatically extracts structured data from supported file formats, normalizes, and converts data from these formats. NOMAD provides an extendable framework for managing not just files, but structured machine-actionable harmonized and inter-operable data. This is the basis for a faceted search with domain-specific filters, a comprehensive API, structured data entry via customizable ELNs, integrated data-analysis and machine-learning tools. NOMAD is run as a free public service and can additionally be operated by research institutes. Connecting NOMAD installations through the public services will allow a federated data infrastructure to share data between research institutes and further harmonize RDM within a large research domain such as materials scienc

Finding predictive models for singlet fission by machine learning - Liu, Xingyu; Wang, Xiaopeng; Gao, Siyu; Chang, Vincent; Tom, Rithwik; Yu, Maituo; Ghiringhelli, Luca M.; Marom, Noa (npj Computational Materials, 2022)
Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.
CRIS-Team Scopus Importer:2023-09-25

Formation of water chains on CaO(001): What drives the 1D growth? - Zhao, Xunhua; Shao, Xiang; Fujimori, Yuichi; Bhattacharya, Saswata; Ghiringhelli, Luca M.; Freund, Hans Joachim; Sterrer, Martin; Nilius, Niklas; Levchenko, Sergey V. (Journal of Physical Chemistry Letters, 2015)
Formation of partly dissociated water chains is observed on CaO(001) films upon water exposure at 300 K. While morphology and orientation of the 1D assemblies are revealed from scanning tunneling microscopy, their atomic structure is identified with infrared absorption spectroscopy combined with density functional theory calculations. The latter exploit an ab initio genetic algorithm linked to atomistic thermodynamics to determine low-energy H2O configurations on the oxide surface. The development of 1D structures on the C4v symmetric CaO(001) is triggered by symmetry-broken water tetramers and a favorable balance between adsorbate-adsorbate versus adsorbate-surface interactions at the constraint of the CaO lattice parameter.
CRIS-Team Scopus Importer:2023-09-25
Keywords: 1D molecular assemblies; DFT global optimization; genetic algorithm; IR spectroscopy; oxide surfaces; scanning tunneling microscopy; water adsorption

Free gold clusters: Beyond the static, monostructure description - Beret, Elizabeth C.; Ghiringhelli, Luca M.; Scheffler, Matthias (Faraday Discussions, 2011)
The thermodynamical stability of free, pristine gold clusters at finite temperature, and of cluster+ligands complexes at finite temperature and in the presence of an atmosphere composed of O2 and CO, is studied employing parallel tempering and ab initio atomistic thermodynamics. We focus on Au 13, which displays a significant fluxional behavior: Even at low temperature (100 K) this cluster exhibits a multitude of structures that dynamically transform into each other. At finite temperature, the preference of this cluster for three-dimensional versus planar structures is found to result from entropic effects. For gold clusters containing one to four gold atoms in an O2 + CO atmosphere, we apply ab initio atomistic thermodynamics. On the basis of these considerations, we single out a likely reaction path for CO oxidation catalyzed by gold clusters. © 2011 The Royal Society of Chemistry.
CRIS-Team Scopus Importer:2023-09-25

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction - Curtis, Farren; Li, Xiayue; Rose, Timothy; Vázquez-Mayagoitia, Álvaro; Bhattacharya, Saswata; Ghiringhelli, Luca M.; Marom, Noa (Journal of Chemical Theory and Computation, 2018)
We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z′ = 2 structure with P1symmetry and a scaffold packing motif, which has not been reported previously.
CRIS-Team Scopus Importer:2023-09-25

How big is big data? - Speckhard, Daniel; Bechtel, Tim; Ghiringhelli, Luca; Kuban, Martin; Rigamonti, Santiago; Draxl, Claudia (Faraday Discussions, 2024)
Big data has ushered in a new wave of predictive power using machine-learning models. In this work, we assess what big means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.
CRIS-Team Scopus Importer:2024-10-18

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery - Boley, Mario; Goldsmith, Bryan R.; Ghiringhelli, Luca M.; Vreeken, Jilles (Data Mining and Knowledge Discovery, 2017)
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Branch-and-bound search; Local pattern discovery; Subgroup discovery

Identifying domains of applicability of machine learning models for materials science - Sutton, Christopher; Boley, Mario; Ghiringhelli, Luca M.; Rupp, Matthias; Vreeken, Jilles; Scheffler, Matthias (Nature Communications, 2020)
Although machine learning (ML) models promise to substantially accelerate the discovery of novel materials, their performance is often still insufficient to draw reliable conclusions. Improved ML models are therefore actively researched, but their design is currently guided mainly by monitoring the average model test error. This can render different models indistinguishable although their performance differs substantially across materials, or it can make a model appear generally insufficient while it actually works well in specific sub-domains. Here, we present a method, based on subgroup discovery, for detecting domains of applicability (DA) of models within a materials class. The utility of this approach is demonstrated by analyzing three state-of-the-art ML models for predicting the formation energy of transparent conducting oxides. We find that, despite having a mutually indistinguishable and unsatisfactory average error, the models have DAs with distinctive features and notably improved performance.
CRIS-Team Scopus Importer:2023-09-25

Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery - Foppa, Lucas; Ghiringhelli, Luca M. (Topics in Catalysis, 2022)
In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation of adsorption energies compared to first-principles calculations. However, global models designed to describe as many materials as possible might overlook the very few compounds that have the appropriate adsorption properties to be suitable for a given catalytic process. Here, the subgroup-discovery (SGD) local artificial-intelligence approach is used to identify the key descriptive parameters and constrains on their values, the so-called SG rules, which particularly describe transition-metal surfaces with outstanding adsorption properties for the oxygen-reduction and -evolution reactions. We start from a data set of 95 oxygen adsorption-energy values evaluated by density-functional-theory calculations for several monometallic surfaces along with 16 atomic, bulk and surface properties as candidate descriptive parameters. From this data set, SGD identifies constraints on the most relevant parameters describing materials and adsorption sites that (i) result in O adsorption energies within the Sabatier-optimal range required for the oxygen-reduction reaction and (ii) present the largest deviations from the linear-scaling relations between O and OH adsorption energies, which limit the catalyst performance in the oxygen-evolution reaction. The SG rules not only reflect the local underlying physicochemical phenomena that result in the desired adsorption properties, but also guide the challenging design of alloy catalysts.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Artificial intelligence; Subgroup discovery; Supervised descriptive rule induction; Symbolic inference; Transition-metal surfaces

Information-theoretic approach to kinetic-energy functionals: The nearly uniform electron gas - Ghiringhelli, Luca M.; Site, Luigi Delle; Mosna, Ricardo A.; Hamilton, I. P. (Journal of Mathematical Chemistry, 2010)
We strengthen the connection between information theory and quantum mechanical systems using a recently developed dequantization procedure which results in a decomposition of the kinetic energy as the sum of a classical term and a purely quantum term. For the nearly uniform electron gas, we thereby approximate the noninteracting kinetic energy as the sum of the Thomas-Fermi term, which is exact for the uniform electron gas, and the Weizsäcker term, which is proportional to the Fisher information. Electron correlation is included via a nonlocal analytical expression which is a functional of the (N-1)-conditional probability density. This expression is evaluated via a statistically rigorous Monte-Carlo procedure to obtain the correlation energy as a functional of the electron density. We show that this functional is well aproximated by a term which is proportional to the Shannon entropy. Thus the kinetic energy is expressed as the standard Thomas-Fermi term plus terms which are proportional to two of the cornerstones of information theory: the Fisher information, which is a measure of localization, and the Shannon entropy, which is a measure of delocalization. © 2010 Springer Science+Business Media, LLC.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Fisher information; Kinetic-energy functionals; Shannon entropy

Insightful classification of crystal structures using deep learning - Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias; Ghiringhelli, Luca M. (Nature Communications, 2018)
Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep learning neural network model for classification. Our approach is able to correctly classify a dataset comprising more than 100,000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.
CRIS-Team Scopus Importer:2023-09-25

Intense fluorescence of Au20 - Yu, Chongqi; Harbich, Wolfgang; Sementa, Luca; Ghiringhelli, Luca M.; Aprá, Edoardo; Stener, Mauro; Fortunelli, Alessandro; Brune, Harald (Journal of Chemical Physics, 2017)
Ligand-protected Au clusters are non-bleaching fluorescence markers in bio- and medical applications. Here we show that their fluorescence can be an intrinsic property of the Au cluster itself. We find a very intense and sharp fluorescence peak located at λ=739.2 nm (1.68 eV) for Au20 clusters in a Ne matrix held at 6 K. The fluorescence reflects the Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital (HOMO-LUMO) diabatic bandgap of the cluster. Au20 shows a very rich absorption fine structure reminiscent of well defined molecule-like quantum levels. These levels are resolved since Au20 has only one stable isomer (tetrahedral); therefore our sample is mono-disperse in cluster size and conformation. Density-functional theory (DFT) and time-dependent DFT calculations clarify the nature of optical absorption and predict both main absorption peaks and intrinsic fluorescence in fair agreement with experiment.
CRIS-Team Scopus Importer:2023-09-25

Interacting electrons, spin statistics, and information theory - Ghiringhelli, L. M.; Hamilton, I. P.; Delle Site, L. (Journal of Chemical Physics, 2010)
We consider a nearly (or quasi) uniform gas of interacting electrons for which spin statistics play a crucial role. A previously developed procedure, based on the extension of the Levy-Lieb constrained search principle and Monte Carlo sampling of electron configurations in space, allows us to approximate the form of the kinetic-energy functional. For a spinless electron gas, this procedure led to a correlation term, which had the form of the Shannon entropy, but the resulting kinetic-energy functional does not satisfy the Lieb-Thirring inequality, which is rigorous and one of the most general relations regarding the kinetic energy. In this paper, we show that when the fermionic character of the electrons is included via a statistical spin approach, our procedure leads to correlation terms, which also have the form of the Shannon entropy and the resulting kinetic-energy functional does satisfy the Lieb-Thirring inequality. In this way we further strengthen the connection between Shannon entropy and electron correlation and, more generally, between information theory and quantum mechanics. © 2010 American Institute of Physics.
CRIS-Team Scopus Importer:2023-09-25

Investigating the ranges of (meta)stable phase formation in (InxGa1-x)2 O3: Impact of the cation coordination - Wouters, C.; Sutton, C.; Ghiringhelli, Luca M.; Markurt, T.; Schewski, R.; Hassa, A.; Von Wenckstern, H.; Grundmann, M.; Scheffler, M.; Albrecht, M. (Physical Review Materials, 2020)
We investigate the phase diagram of the heterostructural solid solution (InxGa1-x)2O3 both computationally, by combining cluster expansion and density functional theory, and experimentally, by means of transmission electron microscopy (TEM) measurements of pulsed laser deposited (PLD) heteroepitaxial thin films. The shapes of the Gibbs free energy curves for the monoclinic, hexagonal, and cubic bixbyite alloy as a function of composition can be explained in terms of the preferred cation coordination environments of indium and gallium. We show by atomically resolved scanning TEM that the strong preference of indium for sixfold coordination results in ordered monoclinic and hexagonal lattices. This ordering impacts the configurational entropy in the solid solution and thereby the (InxGa1-x)2O3 phase diagram. The resulting phase diagram is characterized by very limited solubilities of gallium and indium in the monoclinic, hexagonal, and cubic ground state phases, respectively, but exhibits wide metastable ranges at realistic growth temperatures. On the indium rich side of the phase diagram a wide miscibility gap up to temperatures higher than 1400 K is found, which results in phase separated layers. The experimentally observed indium solubilities in the PLD samples are in the range of x=0.45 and x=0.55 for monoclinic and hexagonal single-phase films, while for phase separated films we find x=0.5 for the monoclinic phase, x=0.65-0.7 for the hexagonal phase and x≥0.9 for the cubic phase. These values are consistent with the computed metastable ranges for each phase.
CRIS-Team Scopus Importer:2023-09-25

Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence - Foppa, Lucas; Sutton, Christopher; Ghiringhelli, Luca M.; De, Sandip; Löser, Patricia; Schunk, Stephan A.; Schäfer, Ansgar; Scheffler, Matthias (ACS Catalysis, 2022)
The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small in comparison to the number of possible materials. Here, we show how the subgroup-discovery (SGD) artificial-intelligence approach can be applied to an experimental plus theoretical data set to identify constraints on key physicochemical parameters, the so-called SG rules, which exclusively describe materials and reaction conditions with outstanding catalytic performance. By using high-throughput experimentation, 120 SiO2-supported catalysts containing ruthenium, tungsten, and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and 10 parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields toward the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated with high performance but also guide the design of more complex catalysts containing up to five elements in their composition.
CRIS-Team Scopus Importer:2023-09-25
Keywords: artificial intelligence; high-throughput experimentation; propylene; ruthenium; selective oxidation; subgroup discovery

Learning physical descriptors for materials science by compressed sensing - Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias (New Journal of Physics, 2017)
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.
CRIS-Team Scopus Importer:2023-09-25
Keywords: big-data driven materials science; compressed sensing; crystal-structure prediction; feature selection

Levy-Lieb-based Monte Carlo study of the dimensionality behaviour of the electronic kinetic functional - Seshaditya, A.; Ghiringhelli, Luca M.; Site, Luigi Delle (Computation, 2017)
We consider a gas of interacting electrons in the limit of nearly uniform density and treat the one dimensional (1D), two dimensional (2D) and three dimensional (3D) cases. We focus on the determination of the correlation part of the kinetic functional by employing a Monte Carlo sampling technique of electrons in space based on an analytic derivation via the Levy-Lieb constrained search principle. Of particular interest is the question of the behaviour of the functional as one passes from 1D to 3D; according to the basic principles of Density Functional Theory (DFT) the form of the universal functional should be independent of the dimensionality. However, in practice the straightforward use of current approximate functionals in different dimensions is problematic. Here, we show that going from the 3D to the 2D case the functional form is consistent (concave function) but in 1D becomes convex; such a drastic difference is peculiar of 1D electron systems as it is for other quantities. Given the interesting behaviour of the functional, this study represents a basic first-principle approach to the problem and suggests further investigations using highly accurate (though expensive) many-electron computational techniques, such as Quantum Monte Carlo.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Dimensionality; Kinetic-energy functionals; Levy-Lieb principle; Monte Carlo sampling of electrons

Main-group test set for materials science and engineering with user-friendly graphical tools for error analysis: Systematic benchmark of the numerical and intrinsic errors in state-of-the-art electronic-structure approximations - Zhang, Igor Ying; Logsdail, Andrew J.; Ren, Xinguo; Levchenko, Sergey V.; Ghiringhelli, Luca M.; Scheffler, Matthias (New Journal of Physics, 2019)
Understanding the applicability and limitations of electronic-structure methods needs careful and efficient comparison with accurate reference data. Knowledge of the quality and errors of electronic-structure calculations is crucial to advanced method development, high-throughput computations and data analyses. In this paper, we present a main-group test set for computational materials science and engineering (MSE), that provides accurate and easily accessible crystal properties for a hierarchy of exchange-correlation approximations, ranging from the well-established mean-field approximations to the state-of-the-art methods of many-body perturbation theory. We consider cohesive energy, lattice constant and bulk modulus of a set of materials that representatives for the first- and second-row elements and their binaries with cubic crystal structures and various bonding characters. A strong effort is made to achieve high numerical accuracy for cohesive properties as calculated using the local-density approximation (LDA), several generalized gradient approximations (GGAs), meta-GGAs and hybrids in all-electron resolution, and the second-order Moller-Plesset perturbation theory (MP2) and the random-phase approximation (RPA) both with frozen-core approximation based on all-electron Hartree-Fock, PBE and/or PBE0 references. This results in over 10 000 calculations, which record a comprehensive convergence test with respect to numerical parameters for a wide range of electronic-structure methods within the numerical atom-centered orbital framework. As an indispensable part of the MSE test set, a web site is established http://mse.fhi-berlin.mpg.de. This not only allows for easy access to all reference data but also provides user-friendly graphical tools for post-processing error analysis.
CRIS-Team Scopus Importer:2023-09-25
Keywords: density functional theory; many-body perturbation theory; random-phase approximation; test set

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence - Foppa, Lucas; Ghiringhelli, Luca M.; Girgsdies, Frank; Hashagen, Maike; Kube, Pierre; Hävecker, Michael; Carey, Spencer J.; Tarasov, Andrey; Kraus, Peter; Rosowski, Frank; Schlögl, Robert; Trunschke, Annette; et al. (Mrs Bulletin, 2021)
Abstract: The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters (“materials genes”) reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of “clean data,” containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. Impact statement: Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Artificial intelligence; Catalytic; Data; Materials genome; Surface chemistry

(Meta-)stability and Core-Shell Dynamics of Gold Nanoclusters at Finite Temperature - Guedes-Sobrinho, Diego; Wang, Weiqi; Hamilton, Ian P.; Da Silva, Juarez L.F.; Ghiringhelli, Luca M. (Journal of Physical Chemistry Letters, 2019)
Gold nanoclusters have been the focus of numerous computational studies, but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core-shell morphology is observed. We analyze their structure and dynamics using state-of-the-art techniques, including unsupervised machine-learning nonlinear dimensionality reduction (sketch-map) for describing the similarities and differences among the range of sampled configurations. In the examined temperature range between 300 and 600 K, we find that whereas the gold nanoclusters exhibit continuous structural rearrangement, they are not amorphous. Instead, they clearly show persistent motifs: a cationic core of 1-5 atoms is loosely bound to a shell which typically displays a substructure resulting from the competition between locally spherical versus planar fragments. Besides illuminating the properties of core-shell gold nanoclusters, the present study proposes a set of useful tools for understanding their nature in operando. ©
CRIS-Team Scopus Importer:2023-09-25

New tolerance factor to predict the stability of perovskite oxides and halides - Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.; Ouyang, Runhai; Musgrave, Charles B.; Ghiringhelli, Luca M.; Scheffler, Matthias (Science Advances, 2019)
Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, t, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX 3 materials (X = O 2− , F − , Cl − , Br − , I − ) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). t is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A 2 BB′X 6 ) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.
CRIS-Team Scopus Importer:2023-09-25

Not so loosely bound rare gas atoms: Finite-temperature vibrational fingerprints of neutral gold-cluster complexes - Ghiringhelli, Luca M.; Gruene, Philipp; Lyon, Jonathan T.; Rayner, David M.; Meijer, Gerard; Fielicke, André; Scheffler, Matthias (New Journal of Physics, 2013)
We present an experimental and theoretical study of the structure of small, neutral gold clusters - Au3, Au4 and Au7 - 'tagged' by krypton atoms. Infrared (IR) spectra of AuN· KrM complexes formed at 100 K are obtained via far-IR multiple photon dissociation in a molecular beam. The theoretical study is based on a statistical (canonical) sampling of the AuN·KrM complexes through ab initio molecular dynamics using density-functional theory in the generalized gradient approximation, explicitly corrected for long-range van-der-Waals (vdW) interactions. The choice of the functional is validated against higher-level first-principle methods. Thereby finite-temperature theoretical vibrational spectra are obtained that are compared with the experimental spectra. This enables us to identify which structures are present in the experimental molecular beam for a given cluster size. For Au2, Au3 and Au4, the predicted vibrational spectra of the Kr-complexed and pristine species differ. For Au7, the presence of Kr influences the vibrational spectra only marginally. This behavior is explained in terms of the formation of a weak chemical bond between Kr and small gold clusters that localizes the Kr atom at a defined adsorption site, whereas for bigger clusters the vdW interactions prevail and the Kr adatom is delocalized and orbits the gold cluster. In all cases, at temperatures as low as T = 100 K, vibrational spectra already display a notable anharmonicity and show, in comparison with harmonic spectra, different position of the peaks, different intensities and broadenings, and even the appearance of new peaks. © IOP Publishing and Deutsche Physikalische Gesellschaft.
CRIS-Team Scopus Importer:2023-09-25

Numerical quality control for DFT-based materials databases - Carbogno, Christian; Thygesen, Kristian Sommer; Bieniek, Björn; Draxl, Claudia; Ghiringhelli, Luca M.; Gulans, Andris; Hofmann, Oliver T.; Jacobsen, Karsten W.; Lubeck, Sven; Mortensen, Jens Jørgen; Strange, Mikkel; Wruss, Elisabeth; et al. (npj Computational Materials, 2022)
Electronic-structure theory is a strong pillar of materials science. Many different computer codes that employ different approaches are used by the community to solve various scientific problems. Still, the precision of different packages has only been scrutinized thoroughly not long ago, focusing on a specific task, namely selecting a popular density functional, and using unusually high, extremely precise numerical settings for investigating 71 monoatomic crystals1. Little is known, however, about method- and code-specific uncertainties that arise under numerical settings that are commonly used in practice. We shed light on this issue by investigating the deviations in total and relative energies as a function of computational parameters. Using typical settings for basis sets and k-grids, we compare results for 71 elemental1 and 63 binary solids obtained by three different electronic-structure codes that employ fundamentally different strategies. On the basis of the observed trends, we propose a simple, analytical model for the estimation of the errors associated with the basis-set incompleteness. We cross-validate this model using ternary systems obtained from the Novel Materials Discovery (NOMAD) Repository and discuss how our approach enables the comparison of the heterogeneous data present in computational materials databases.
CRIS-Team Scopus Importer:2023-09-25

Proton wires via one-dimensional water chains adsorbed on metallic steps - Scipioni, R.; Donadio, D.; Ghiringhelli, L. M.; Delle Site, L. (Journal of Chemical Theory and Computation, 2011)
The process of proton transfer is here analyzed for one-dimensional water chains adsorbed on metallic steps. When the water chain contains a hydronium and a hydroxyl ion, two different mechanisms are possible, depending on the metal substrate. On coinage metals (Ag, Au), recombination is observed through a spontaneous Grotthuss mechanism. On more reactive surfaces (Pd and Pt), the hydronium ion is unstable and releases a proton that adsorbs onto the metal, leaving the negatively charged OH- unbalanced. In this case, the negative charge can be transferred along the wire with very low activation barriers. © 2011 American Chemical Society.
CRIS-Team Scopus Importer:2023-09-25

Reaction cycles and poisoning in catalysis by gold clusters: A thermodynamics approach - Beret, Elizabeth C.; Van Wijk, Merel M.; Ghiringhelli, Luca M. (International Journal of Quantum Chemistry, 2014)
In heterogeneous catalysis, a catalytic process takes place at finite temperature and at finite pressure of the atmosphere of the reactant gases. By applying ab initio atomistic thermodynamics to the model case of free Au 2 and Au2- clusters in an atmosphere of O2 and CO, we derive all the thermodynamically possible reaction paths for the oxidation of CO to CO2. This analysis lets us explain how gold clusters enable oxidation reactions without breaking the spin-conservation rule. Furthermore, we identify special cluster + ligands compositions such as reaction intermediates and poisoned species. In particular, a thermodynamically driven poisoning is identified for the catalytic system containing free Au2, and the experimental (p, T) conditions that avoid its formation are suggested. This implies that for some systems a catalytic cycle can be established, on thermodynamics grounds, only in a defined range of temperatures and pressures. In addition, our predictions for Au2- provide the so far most complete interpretation of the available experimental data (Socaciu et al, J. Am. Chem. Soc. 2003). © 2013 Wiley Periodicals, Inc. Heterogeneous catalytic processes take place at the finite temperature and pressure of the atmosphere of the reactant gases. By applying ab initio atomistic thermodynamics, it is possible to derive all the thermodynamically possible reaction paths for the oxidation of CO to CO2 catalyzed by the model clusters Au2- and Au2. In the latter case, a thermodynamically driven poisoning is identified, and the experimental (p, T) conditions that avoid its formation are suggested. Copyright © 2013 Wiley Periodicals, Inc.
CRIS-Team Scopus Importer:2023-09-25
Keywords: ab initio atomistic thermodynamics; CCSD(T); clusters; density functional theory; heterogenous catalysis

Report on the sixth blind test of organic crystal structure prediction methods - Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; et al. (Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 2016)
The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and 'best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z′ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The results of the sixth blind test of organic crystal structure prediction methods are presented and discussed, highlighting progress for salts, hydrates and bulky flexible molecules, as well as on-going challenges.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Cambridge Structural Database; crystal structure prediction; lattice energies; polymorphism

Roadmap on data-centric materials science - Bauer, Stefan; Benner, Peter; Bereau, Tristan; Blum, Volker; Boley, Mario; Carbogno, Christian; Catlow, C. A.Richard; Dehm, Gerhard; Eibl, Sebastian; Ernstorfer, Ralph; Fekete, Ádám; Foppa, Lucas; et al. (Modelling and Simulation in Materials Science and Engineering, 2024)
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
CRIS-Team Scopus Importer:2024-08-02
Keywords: centric; data; materials; molecular simulations; roadmap; science

Roadmap on Machine learning in electronic structure - Kulik, H. J.; Hammerschmidt, Thomas; Schmidt, Jonathan; Botti, Silvana; Marques, M. A. L.; Boley, M.; Scheffler, M.; Todorovic, Milica; Rinke, Patrick; Oses, Corey; Smolyanyuk, A.; Curtarolo, S.; et al. (Electronic Structure, 2022)
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.
CRIS-Team Scopus Importer:2023-04-20
Keywords: computational materials science; density-functional theory; electronic structure; machine learning

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning - Leitherer, Andreas; Ziletti, Angelo; Ghiringhelli, Luca M. (Nature Communications, 2021)
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.
CRIS-Team Scopus Importer:2023-09-25

Shared metadata for data-centric materials science - Ghiringhelli, Luca M.; Baldauf, Carsten; Bereau, Tristan; Brockhauser, Sandor; Carbogno, Christian; Chamanara, Javad; Cozzini, Stefano; Curtarolo, Stefano; Draxl, Claudia; Dwaraknath, Shyam; Fekete, Ádám; Kermode, James; et al. (Scientific Data, 2023)
CRIS-Team Scopus Importer:2023-09-22

Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO - Ouyang, Runhai; Ahmetcik, Emre; Carbogno, Christian; Scheffler, Matthias; Ghiringhelli, Luca M. (JPhys Materials, 2019)
The identification of descriptors of materials properties and functions that capture the underlying physical mechanisms is a critical goal in data-driven materials science. Only such descriptors will enable a trustful and efficient scanning of materials spaces and possibly the discovery of new materials. Recently, the sure-independence screening and sparsifying operator (SISSO) has been introduced and was successfully applied to a number of materials-science problems. SISSO is a compressed sensing based methodology yielding predictive models that are expressed in form of analytical formulas, built from simple physical properties. These formulas are systematically selected from an immense number (billions or more) of candidates. In this work, we describe a powerful extension of the methodology to a 'multi-task learning' approach, which identifies a single descriptor capturing multiple target materials properties at the same time. This approach is specifically suited for a heterogeneous materials database with scarce or partial data, e.g. in which not all properties are reported for all materials in the training set. As showcase examples, we address the construction of materials properties maps for the relative stability of octet-binary compounds, considering several crystal phases simultaneously, and the metal/insulator classification of binary materials distributed over many crystal prototypes.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Artificial intelligence; Compressed sensing; Crystal structure prediction; Metal/nonmetal classification

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates - Ouyang, Runhai; Curtarolo, Stefano; Ahmetcik, Emre; Scheffler, Matthias; Ghiringhelli, Luca M. (Physical Review Materials, 2018)
The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a material's property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.
CRIS-Team Scopus Importer:2023-09-25

Stability and metastability of clusters in a reactive atmosphere: Theoretical evidence for unexpected stoichiometries of MgMO x - Bhattacharya, Saswata; Levchenko, Sergey V.; Ghiringhelli, Luca M.; Scheffler, Matthias (Physical Review Letters, 2013)
By applying a genetic algorithm and ab initio atomistic thermodynamics, we identify the stable and metastable compositions and structures of Mg MOx clusters at realistic temperatures and oxygen pressures. We find that small clusters (M 5) are in thermodynamic equilibrium when x>M. The nonstoichiometric clusters exhibit peculiar magnetic behavior, suggesting the possibility of tuning magnetic properties by changing environmental pressure and temperature conditions. Furthermore, we show that density-functional theory with a hybrid exchange-correlation functional is needed for predicting accurate phase diagrams of metal-oxide clusters. Neither a (sophisticated) force field nor density-functional theory with (semi)local exchange-correlation functionals is sufficient for even a qualitative prediction. Published by American Physical Society. Published by the American Physical Society under the terms of the http://creativecommons.org/licenses/by/ 3.0/ Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
CRIS-Team Scopus Importer:2023-09-25

Strengthening gold-gold bonds by complexing gold clusters with noble gases - Ghiringhelli, Luca M.; Levchenko, Sergey V. (Inorganic Chemistry Communications, 2015)
We report an unexpectedly strong and complex chemical bonding of rare-gas atoms to neutral gold clusters. The bonding features are consistently reproduced at different levels of approximation within density-functional theory and beyond: from GGA, through hybrid and double-hybrid functionals, up to renormalized second-order perturbation theory. The main finding is that the adsorption of Ar, Kr, and Xe reduces electron-electron repulsion within gold dimer, causing strengthening of the AuAu bond. Differently from the dimer, the rare-gas adsorption effects on the gold trimer's geometry and vibrational frequencies are mainly due to electron occupation of the trimer's lowest unoccupied molecular orbital. For the trimer, the theoretical results are also consistent with far-infrared multiple photon dissociation experiments.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Covalently bonded rare-gas complexes; Electronic structure calculations; Neutral gold clusters; Vibrational spectroscopy

Structure and Electronic Properties of Transition-Metal/Mg Bimetallic Clusters at Realistic Temperatures and Oxygen Partial Pressures - Saini, Shikha; Sarker, Debalaya; Basera, Pooja; Levchenko, Sergey V.; Ghiringhelli, Luca M.; Bhattacharya, Saswata (Journal of Physical Chemistry C, 2018)
Composition, atomic structure, and electronic properties of TMxMgyOz clusters [transition metal (TM) = Cr, Ni, Fe, and Co, x + y ≤ 3] under realistic temperature T and oxygen partial pressure pO2 conditions are explored using the ab initio atomistic thermodynamics approach. The low-energy isomers of the different clusters are identified using a massively parallel cascade genetic algorithm on the hybrid density-functional theory level. On analyzing a large set of data, we find that the fundamental gap Eg of the thermodynamically stable clusters is strongly affected by the presence of Mg-coordinated O2 moieties. By contrast, the nature of the TM does not play a significant role in determining Eg. Using Eg of a cluster as a descriptor of its redox properties, our finding is against the conventional belief that the TM plays a key role in determining the electronic and therefore chemical properties of the clusters. High reactivity may be correlated more strongly with the oxygen content in the cluster than with any specific TM type.
CRIS-Team Scopus Importer:2023-09-25

Surface-induced crystallization in supercooled tetrahedral liquids - Li, Tianshu; Donadio, Davide; Ghiringhelli, Luca M.; Galli, Giulia (Nature Materials, 2009)
Surfaces have long been known to have an intricate role in solid-liquid phase transformations. Whereas melting is often observed to originate at surfaces, freezing usually starts in the bulk, and only a few systems have been reported to exhibit signatures of surface-induced crystallization. These include assembly of chain-like molecules2, some liquid metals and alloys3-5 and silicate glasses6,7. Here, we report direct computational evidence of surface-induced nucleation in supercooled liquid silicon and germanium, and we illustrate the crucial role of free surfaces in the freezing process of tetrahedral liquids exhibiting a negative slope of their melting lines (dT/dP|coexist >0). Our molecular dynamics simulations show that the presence of free surfaces may enhance the nucleation rates by several orders of magnitude with respect to those found in the bulk. Our findings provide insight, at the atomistic level, into the nucleation mechanism of widely used semiconductors, and support the hypothesis of surface-induced crystallization in other tetrahedrally coordinated systems, in particular water in the atmosphere. © 2009 Macmillan Publishers Limited. All rights reserved.
CRIS-Team Scopus Importer:2023-09-25

TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions - Regler, Benjamin; Scheffler, Matthias; Ghiringhelli, Luca M. (Data Mining and Knowledge Discovery, 2022)
The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the analysis of data sets with a large number of variables. A mathematical rigorous approach to quantifying the relevance of these features is mutual information. Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest. However, mutual information requires as input probability distributions, which cannot be reliably estimated from continuous distributions such as physical quantities like lengths or energies. Here, we introduce total cumulative mutual information (TCMI), a measure of the relevance of mutual dependences that extends mutual information to random variables of continuous distribution based on cumulative probability distributions. TCMI is a non-parametric, robust, and deterministic measure that facilitates comparisons and rankings between feature sets with different cardinality. The ranking induced by TCMI allows for feature selection, i.e., the identification of variable sets that are nonlinear statistically related to a property of interest, taking into account the number of data samples as well as the cardinality of the set of variables. We evaluate the performance of our measure with simulated data, compare its performance with similar multivariate-dependence measures, and demonstrate the effectiveness of our feature-selection method on a set of standard data sets and a typical scenario in materials science.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Dependence measure; Feature selection; Information theory; Machine learning; Materials science; Mutual information

The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding - Sbailò, Luigi; Fekete, Ádám; Ghiringhelli, Luca M.; Scheffler, Matthias (npj Computational Materials, 2022)
We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive, the largest database of materials-science data worldwide, as well as locally stored, users’ owned data. The NOMAD Oasis, a local, stand-alone server can be also used to run the AI Toolkit. By using Jupyter notebooks that run in a web-browser, the NOMAD data can be queried and accessed; data mining, machine learning, and other AI techniques can be then applied to analyze them. This infrastructure brings the concept of reproducibility in materials science to the next level, by allowing researchers to share not only the data contributing to their scientific publications, but also all the developed methods and analytics tools. Besides reproducing published results, users of the NOMAD AI toolkit can modify the Jupyter notebooks toward their own research work.
CRIS-Team Scopus Importer:2023-09-25

Theoretical evidence for unexpected O-rich phases at corners of MgO surfaces - Bhattacharya, Saswata; Berger, Daniel; Reuter, Karsten; Ghiringhelli, Luca M.; Levchenko, Sergey V. (Physical Review Materials, 2017)
Realistic oxide materials are often semiconductors, in particular at elevated temperatures, and their surfaces contain undercoordinated atoms at structural defects such as steps and corners. Using hybrid density-functional theory and ab initio atomistic thermodynamics, we investigate the interplay of bond-making, bond-breaking, and charge-carrier trapping at the corner defects at the (100) surface of a p-doped MgO in thermodynamic equilibrium with an O2 atmosphere. We show that by manipulating the coordination of surface atoms, one can drastically change and even reverse the order of stability of reduced versus oxidized surface sites.
CRIS-Team Scopus Importer:2023-09-25

Theory and hierarchical calculations of the structure and energetics of [0001] tilt grain boundaries in graphene - Carlsson, Johan M.; Ghiringhelli, Luca M.; Fasolino, Annalisa (Physical Review B - Condensed Matter and Materials Physics, 2011)
Several experiments have revealed the presence of grain boundaries in graphene that may change its electronic and elastic properties. Here, we present a general theory for the structure of [0001] tilt grain boundaries in graphene based on the coincidence site lattice (CSL) theory. We show that the CSL theory uniquely classifies the grain boundaries in terms of the misorientation angle θ and periodicity d using two grain-boundary indices (m,n), similar to the nanotube indices. The structure and formation energy of a large set of grain boundaries generated by the CSL theory for 0<θ<60 (up to 15 608 atoms) were optimized by a hierarchical methodology and validated by density functional calculations. We find that low-energy grain boundaries in graphene can be identified as dislocation arrays. The dislocations form hillocks like those observed by scanning tunneling microscopy in graphene grown on Ir(111) for small θ that flatten out at larger misorientation angles. We find that, in contrast to three-dimensional materials, the strain created by the grain boundary can be released via out-of-plane distortions that lead to an effective attractive interaction between dislocation cores. Therefore, the dependence on θ of the formation energy parallels that of the out-of-plane distortions, with a secondary minimum at θ=32.2 where the grain boundary is made of a flat zigzag array of only 5 and 7 rings. For θ>32.2, other nonhexagonal rings are also possible. We discuss the importance of these findings for the interpretation of recent experimental results. © 2011 American Physical Society.
CRIS-Team Scopus Importer:2023-09-25

Towards efficient data exchange and sharing for big-data driven materials science: Metadata and data formats - Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias (npj Computational Materials, 2017)
With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.
CRIS-Team Scopus Importer:2023-09-25

Towards Experimental Handbooks in Catalysis - Trunschke, Annette; Bellini, Giulia; Boniface, Maxime; Carey, Spencer J.; Dong, Jinhu; Erdem, Ezgi; Foppa, Lucas; Frandsen, Wiebke; Geske, Michael; Ghiringhelli, Luca M.; Girgsdies, Frank; Hanna, Rania; et al. (Topics in Catalysis, 2020)
The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.
CRIS-Team Scopus Importer:2023-09-25
Keywords: Artificial intelligence; Best practice; Data science; Descriptor; Machine learning; Rigorous protocols; Standard operation procedure

Trends in the adsorption and dissociation of water clusters on flat and stepped metallic surfaces - Peköz, Rengin; Wörner, Svenja; Ghiringhelli, Luca M.; Donadio, Davide (Journal of Physical Chemistry C, 2014)
Understanding the structure and chemical reactivity of water adsorbed at metallic surfaces is very important in many processes such as catalysis, corrosion, and electrochemistry. Using density functional theory calculations, we investigate the adsorption and dissociation of water clusters on flat and stepped surfaces of several transition metals: Rh, Ir, Pd, and Pt. We find that water binds preferentially to the step edges than to terrace sites, thus linear clusters or one-dimensional water wires can be isolated by differential desorption. The clusters formed at the step are stabilized by the cooperative effect of chemical bonds with the metal and hydrogen bonding. The enhanced reactivity of the step edges and the cooperative effect of hydrogen bonding improve the chances of partial dissociation of water clusters. We assess the correlations between adsorption and dissociation energies, observing that they are increased on stepped surfaces. We present a detailed interpretation of water dissociation by analyzing changes in the electronic structure of both water and metals. The identification of trends in the energetics of water dissociation at transition metals is expected to aid the design of better materials for catalysis and fuel cells, where the density of steps at surfaces would be a relevant additional parameter.
CRIS-Team Scopus Importer:2023-09-25

Two-to-three dimensional transition in neutral gold clusters: The crucial role of van der Waals interactions and temperature - Goldsmith, Bryan R.; Florian, Jacob; Liu, Jin Xun; Gruene, Philipp; Lyon, Jonathan T.; Rayner, David M.; Fielicke, André; Scheffler, Matthias; Ghiringhelli, Luca M. (Physical Review Materials, 2019)
We predict the structures of neutral gas phase gold clusters (Aun,n=5-13) at finite temperatures based on free-energy calculations obtained by replica-exchange ab initio molecular dynamics. The structures of neutral Au5-Au13 clusters are assigned at 100 K based on a comparison of experimental far-infrared multiple photon dissociation spectra performed on Kr-tagged gold clusters with theoretical anharmonic IR spectra and free-energy calculations. The critical gold cluster size at which the most stable isomer changes from planar to nonplanar is Au11 (capped-trigonal prism, D3h) at 100 K. However, at 300 K (i.e., room temperature), planar and nonplanar isomers may coexist even for Au8, Au9, and Au10 clusters. Density-functional theory exchange-correlation functionals within the generalized gradient or hybrid approximation must be corrected for long-range van der Waals interactions to accurately predict relative gold cluster isomer stabilities. Our paper gives insight into the stable structures of gas phase gold clusters by highlighting the impact of temperature, and therefore the importance of free energy over total-energy studies, and long-range van der Waals interactions on gold cluster stability.
CRIS-Team Scopus Importer:2023-09-25

Uncovering structure-property relationships of materials by subgroup discovery - Goldsmith, Bryan R.; Boley, Mario; Vreeken, Jilles; Scheffler, Matthias; Ghiringhelli, Luca M. (New Journal of Physics, 2017)
Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. Specifically, we will be concerned with data generated by density-functional theory calculations. At first, we demonstrate that SGD can identify physically meaningful models that classify the crystal structures of 82 octet binary (OB) semiconductors as either rocksalt or zincblende. SGD identifies an interpretable two-dimensional model derived from only the atomic radii of valence s and p orbitals that properly classifies the crystal structures for 79 of the 82 OB semiconductors. The SGD framework is subsequently applied to 24 400 configurations of neutral gas-phase gold clusters with 5-14 atoms to discern general patterns between geometrical and physicochemical properties. For example, SGD helps find that van der Waals interactions within gold clusters are linearly correlated with their radius of gyration and are weaker for planar clusters than for nonplanar clusters. Also, a descriptor that predicts a local linear correlation between the chemical hardness and the cluster isomer stability is found for the even-sized gold clusters.
CRIS-Team Scopus Importer:2023-09-25
Keywords: big-data analytics; data mining; gold clusters; machine learning; octet binary semiconductors; pattern discovery

Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications - Belviso, Florian; Claerbout, Victor E.P.; Comas-Vives, Aleix; Dalal, Naresh S.; Fan, Feng Ren; Filippetti, Alessio; Fiorentini, Vincenzo; Foppa, Lucas; Franchini, Cesare; Geisler, Benjamin; Ghiringhelli, Luca M.; Groß, Axel; et al. (Inorganic Chemistry, 2019)
Nanostructured materials are essential building blocks for the fabrication of new devices for energy harvesting/storage, sensing, catalysis, magnetic, and optoelectronic applications. However, because of the increase of technological needs, it is essential to identify new functional materials and improve the properties of existing ones. The objective of this Viewpoint is to examine the state of the art of atomic-scale simulative and experimental protocols aimed to the design of novel functional nanostructured materials, and to present new perspectives in the relative fields. This is the result of the debates of Symposium I "Atomic-scale design protocols towards energy, electronic, catalysis, and sensing applications", which took place within the 2018 European Materials Research Society fall meeting.
CRIS-Team Scopus Importer:2023-09-25