A generative machine learning model for designing metal hydrides applied to hydrogen storage

TL;DR

This paper introduces a novel generative machine learning framework that combines causal discovery to create and identify new metal hydrides for hydrogen storage, expanding current materials databases efficiently.

Contribution

It presents a new integrated approach using causal discovery and generative modeling to discover unreported metal hydrides for hydrogen storage.

Findings

Generated 1,000 new metal hydride candidates

Identified 6 promising new compounds, 4 validated by DFT

Provides a scalable method for materials discovery

Abstract

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1,000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.