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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title Machine Learning for the Efficient Identification of High-Performance Metal-Doped Transition Metal Compounds for Hydrogen Evolution Catalysis
Author(s) Lu Xue, Jie Dang
On-Site Speaker (Planned) Lu Xue
Abstract Scope Transition metal compounds like nitrides, phosphides, sulfides, and selenides are alternatives to platinum for HER. We propose an ML method to predict HER performance of doped transition metal compounds. Our workflow uses supervised ML to create a predictive model for 360 M1-M2X doped materials, with DFT calculating HER activity for 120 randomly picked systems. Applying feature analysis techniques, we refine key predictors. Models including tree-based, non-tree, and ensemble methods were trained and validated. The XGBR model attained an R² of 0.75 and RMSE of 0.09, demonstrating strong prediction capability for HER in doped materials.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Composites, Computational Materials Science & Engineering

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