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

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 temper

SUBMITTER: Sun Y 

PROVIDER: S-EPMC8294760 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature