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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

An explainable artificial intelligence strategy for materials design

Author(s)

Valentin Vassilev-Galindo, Javier Llorca

On-Site Speaker (Planned)

Javier Llorca

Abstract Scope

The progress of strategies coupling ab initio calculations and machine learning (ML) has opened the door for the virtual design of materials. However, these techniques are very often used with no special attention on how ML models obtain their predictions. This can be improved by explainability of ML models. Explainability is only partially and not consistently addressed in the field. This is striking because, without explainability, the possibility for ML to offer new insights is hindered. Hence, the next generation of ML models must guarantee explainability by embedding explainable artificial intelligence tools into their pipelines. Among the available tools, counterfactuals explanations are ideal for this. They provide insights of model operation by determining cases that explain the difference between a desired and an actual outcome. Here, on the example of heterogeneous catalysts for hydrogen production and energy generation, we propose a novel strategy for materials design based on counterfactuals explanations.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning,

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