Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials

Schmidt J, Hoffmann N, Wang HC, Borlido P, Carriço PJ, Cerqueira TF, Botti S, Marques MA (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Advanced Materials Wiley

DOI: 10.1002/adma.202210788

Abstract

Crystal-graph attention neural networks have emerged recently as remarkable tools for the prediction of thermodynamic stability. The efficacy of their learning capabilities and their reliability is however subject to the quantity and quality of the data they are fed. Previous networks exhibit strong biases due to the inhomogeneity of the training data. Here a high-quality dataset is engineered to provide a better balance across chemical and crystal-symmetry space. Crystal-graph neural networks trained with this dataset show unprecedented generalization accuracy. Such networks are applied to perform machine-learning-assisted high-throughput searches of stable materials, spanning 1 billion candidates. In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom−1. The discovered materials are then accessed for applications, identifying compounds with extreme values of a few properties, such as superconductivity, superhardness, and giant gap-deformation potentials.

Involved external institutions

Martin-Luther-Universität Halle-Wittenberg (MLU) DE Germany (DE) Universidade de Coimbra PT Portugal (PT) Friedrich-Schiller-Universität Jena DE Germany (DE)

How to cite

APA:

Schmidt, J., Hoffmann, N., Wang, H.C., Borlido, P., Carriço, P.J., Cerqueira, T.F.,... Marques, M.A. (2023). Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials. Advanced Materials. https://doi.org/10.1002/adma.202210788

MLA:

Schmidt, Jonathan, et al. "Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials." Advanced Materials (2023).

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