Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning

Sun Y, DeJaco RF, Li Z, Tang D, Glante S, Sholl DS, Colina CM, Snurr RQ, Thommes M, Hartmann M, Ilja Siepmann J (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Science Advances American Association for the Advancement of Science

Book Volume: 7

Article Number: eabg3983

Journal Issue: 30

DOI: 10.1126/sciadv.abg3983

Abstract

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

Authors with CRIS profile

Stephan Glante Interdisziplinäres Zentrum Erlangen Catalysis Resource Center (ECRC) Matthias Thommes Lehrstuhl für Thermische Verfahrenstechnik (TVT) Martin Hartmann Professur für Katalyse

Related research project(s)

Textural properties of porous materials for particle chromatography (SFB 1411 - B03) Design of particulate products (SFB 1411) Jan. 1, 2020 - Dec. 31, 2023 Characterisation of functionalised surfaces (SFB 1411 - C02) Design of particulate products (SFB 1411) Jan. 1, 2020 - Dec. 31, 2023 Nanoparticulate metal-organic frameworks (SFB 1411 - A04) Design of particulate products (SFB 1411) Jan. 1, 2020 - Jan. 1, 2023

Involved external institutions

University of Minnesota (UMN) US United States (USA) (US) Northwestern University US United States (USA) (US) Georgia Institute of Technology US United States (USA) (US) University of Florida US United States (USA) (US)

How to cite

APA:

Sun, Y., DeJaco, R.F., Li, Z., Tang, D., Glante, S., Sholl, D.S.,... Ilja Siepmann, J. (2021). Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning. Science Advances, 7(30). https://doi.org/10.1126/sciadv.abg3983

MLA:

Sun, Yangzesheng, et al. "Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning." Science Advances 7.30 (2021).

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