Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR.

ABSTRACT: The extraordinary progress of strategies coupling ab initio calculations and machine learning (ML) has opened the door for both fast and accurate chemical/physical property predictions and for the virtual design of materials. However, these techniques are very often used as a "black box" with the sole objective of obtaining high accuracy with scarce or no special attention on how ML models obtain their predictions. This can be improved by leveraging explainability of ML models, which, at the same time, would increase the chance of ML to offer new insights into the chemistry and physics of materials. Hence, the next generation of ML models in these realms must guarantee explainability by embedding explainable artificial intelligence (XAI) tools into their pipelines. Specifically, ML-

SUBMITTER: Vassilev-Galindo V 

PROVIDER: S-EPMC12626642 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature