ArticleiScience2026
Explainable machine learning and physics-constrained optimization of chalcogen catalysts for sustainable hydrogen production.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
The accelerating demand for clean energy has intensified research on sustainable hydrogen production, where efficient chalcogen catalyst design remains a major challenge. Most of the existing studies are, however, only concerned with predictive modeling, neglecting physical feasibility and consistency in the optimization process. To overcome this, this study proposes an integrated machine learning and optimization framework, physics-constrained optimization (PCO) and trust-region Bayesian optimization (TRBO), for engineering of chalcogen-based electrocatalysts. The proposed framework takes into account domain-specific physical band gap constraints, such as physically stable formation energies and realistic density bounds, to guarantee physically meaningful and practically feasible solutions. The XGBoost model trained with engineered Magpie descriptors has demonstrated excellent predictive accuracy with R
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