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Decoding structural complexity with machine learning to empower physical AI driven electrocatalyst design
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DOI：10.1016/j.apsadv.2026.100984.png)
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Abstract
En 中文
Heterogeneous catalysis underpins much of the modern chemical industry and energy conversion. Yet, the atomistic modeling frameworks guiding catalyst design have long suffered from the gap between idealized, static representations—such as bulk-truncated surfaces and fixed adsorption geometries—and the complex, evolving nature of real catalytic interfaces under operating conditions. This viewpoint discusses how machine learning (ML) is bridging this gap by reshaping the atomistic modeling of catalytic surfaces. We highlight recent advances in ML for efficient exploration of surface reconstruction, defect evolution, and heterogeneous ensembles of active sites at scales previously inaccessible to first-principles methods. We further examine how these developments expand the accessible configurational and chemical space of catalytic surfaces, enabling high-throughput screening of diverse surface motifs under realistic conditions. Crucially, we examine how this accelerated workflow can be seamlessly integrated with physical AI and autonomous laboratories to overcome long-standing challenges in discovering and synthesizing high-performance, cost-effective materials. By unifying surface science, ML, and robotics, we outline key opportunities and challenges toward predictive, scalable, and physically grounded catalyst design.
Keywords:
Surface catalysis
First-principles calculation
Machine learning
Electrocatalyst design
Data-driven workflow
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