Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction

Abstract

High-entropy alloys (HEAs) have emerged as promising candidates for catalyst applications due to their inherent compositional, structural, and site-level diversities, which enable highly tunable catalytic properties. However, these complexities pose grand challenges for traditional “trial-and-error” experimentation or computationally expensive “brute-force” ab initio calculations. Machine learning (ML) demonstrates great potential to address these challenges by establishing efficient, scalable mappings from composition, structure or site environment to HEA properties. Among these properties, adsorption energy, which quantifies the binding strength between catalytic intermediates and surface sites, is a crucial indicator of catalytic activity. This review provides a comprehensive overview of ML-driven strategies for adsorption energy prediction in the context of HEAs. Two primary strategies are introduced: “direct” prediction from unrelaxed structure and “iterative” prediction via ML potential-guided relaxation modeling. Both strategies can leverage handcrafted features or end-to-end frameworks such as graph neural networks. We also discuss how pretrained models on large-scale databases can extend to out-of-domain HEA systems. Beyond methodology, we address key challenges and future directions, including benchmarking ML strategies, developing HEA-specific datasets, pretraining and fine-tuning, integrating chained ML models, advancing multi-objective optimization, and bridging ML predictions with experimental validation. By critically evaluating existing strategies and highlighting emerging trends, this review underscores the critical role of ML in advancing adsorption energy predictions, offering a foundation for accelerating the discovery and optimization of HEA catalysts.