Machine Learning-Guided Discovery of PGM-Lean High-Entropy Alloys for Efficient Solar Hydrogen Evolution

Hierarchical Site‐to‐Composition Machine Learning for High‐Entropy Electrocatalysts Design

A Hierarchical Site to Composition Machine Learning (HS2C‐ML) framework to accelerate the discovery of efficient HEA catalysts for alkaline hydrogen evolution reaction (HER) using high‐entropy alloys.

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

The sluggish kinetics of the ammonia oxidation reaction constitute a critical bottleneck in the development of low‐temperature direct ammonia fuel cells. High‐entropy alloys (HEAs), owing to their diverse active sites, have emerged as promising catalysts. However, their vast compositional space makes traditional quantum chemical screening prohibitively expensive. In this study, we provide a computational proof of concept showing that the classical d ‐band center theory fails to predict the ammonia oxidation activity of quinary HEAs and exhibits a negligible correlation with the energy barrier of the rate‐determining step. To address this limitation, we employed the Sure Independence Screening and Sparsifying Operator (SISSO) method to construct a transparent and interpretable symbolic descriptor, achieving excellent predictive accuracy ( R 2  = 0.981) within the range of the training data. This descriptor extends beyond simple single‐electron parameters by integrating the synergistic effects of electron‐donating ability, lattice stiffness, and local electronegativity perturbations. This data‐driven approach reduces computational costs by several orders of magnitude relative to exhaustive density functional theory (DFT) screening and identifies an “isolated‐surrounded” geometric configuration as a highly active site that significantly enhances intrinsic catalytic activity from a thermodynamic perspective. Crucially, the predicted motif should be interpreted within the hydrazine‐mediated thermodynamic framework used here, and its thermodynamic superiority may shift if alternative kinetic pathways dominate under operating conditions. This structural motif promotes efficient NN coupling while suppressing site poisoning. Overall, this study provides a coordination chemistry‐based blueprint for the rational design of next‐generation catalysts with reduced platinum‐group‐metal content and offers a theoretical framework for future experimental validation.

Machine Learning‐Accelerated Screening of VCoRu Solid‐Solution Alloy as a Superior Catalyst for Efficient Hydrogen Evolution

Ru‐based solid‐solution alloys have emerged as attractive substitutes for Pt‐based electrocatalysts in the alkaline hydrogen evolution reaction (HER). However, the vast compositional space of these alloys hinders rapid catalyst discovery. Existing machine‐learning design strategies are largely based on idealized simulation data, limiting their experimental relevance and screening efficiency. Herein, we develop an interpretable machine‐learning framework informed by experiment to facilitate the rapid screening of Ru‐based HER electrocatalysts. Built on an experimental dataset, the framework integrates electronic descriptors, interpretability analysis, a Bayesian‐optimized surrogate model and uncertainty‐aware ranking to efficiently identify promising compositions. Interpretability analysis identifies V, Co, and Ru as the key elements governing catalytic performance, whereas the surrogate model maps HER activity across the ternary alloy space to define a confined high‐performance composition window. Guided by this framework, the optimized V 17 Co 31 Ru 52 catalyst exhibits an overpotential of 61.7 mV at a current density of 100 mA·cm −2 in 1 M KOH, while maintaining stable operation for 260 h under the same current density. This work provides a rapid, interpretable and experimentally relevant strategy for discovering high‐performance alkaline HER electrocatalysts.

Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts.

High-entropy oxides (HEOs), which contain multiple cations with diverse valence states and highly disordered local environments, offer a much broader compositional and active-site space than conventional single-component oxides. This diversity creates opportunities for tailoring catalytic properties, but it also makes rational design considerably more difficult. The formation and stability of HEOs phases depend on the combined effects of configurational entropy, mixing enthalpy, oxidation potential, valence compatibility, and synthesis conditions. Meanwhile, their catalytic behavior is often controlled by oxygen vacancies, surface segregation, in situ reconstruction, and dynamically evolving active sites under operating conditions. Artificial intelligence (AI) provides a promising means of managing this complexity by learning relationships among composition, structure, defects, adsorption behavior, and catalytic performance. This review summarizes the major challenges in HEOs catalyst design, discusses data representation and model selection, and examines AI applications in phase stability prediction, hydrogen production, oxygen evolution, thermal catalysis, photocatalysis, and electrocatalyst discovery. It further emphasizes the need to move beyond single-property prediction toward multi-objective optimization and closed-loop integration of AI, DFT, MLIP-based sampling, operando characterization, and experimental validation.

Machine learning-guided discovery of γ-Al2O3 as defect-rich structural ensemble governs single-atom Pt catalysis

Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal–ligand complexes

Hydrogen is central to the global clean energy transition, yet the widespread use of green hydrogen is constrained by its dependence on costly, supply-limited platinum-based catalysts in Proton Exchange Membrane (PEM) electrolyzers. While alternatives have been explored, identifying efficient and stable non-platinum catalysts remains a major challenge due to the vast chemical space and the expense of experimental screening. This study pioneers a novel unsupervised machine learning framework to systematically map this vast chemical space and identify promising catalyst candidates without reliance on pre-existing experimental data. Using the tmQM dataset comprising over 108 000 transition metal–ligand complexes; we selected five key descriptors (HOMO–LUMO gap, dipole moment, molecular size, metal node degree, and total charge) to represent catalytic potential and stability. Three unsupervised clustering algorithms (K-Means, DBSCAN, and Gaussian Mixture Models) were applied to identify chemically and electronically distinct groups. Unlike supervised models that predict properties for known structures, our unsupervised approach uncovers previously unidentified classes of non-platinum complexes that possess features ideal for catalytic activity. This work establishes a new, efficient workflow for catalyst science, shifting the focus from slow, one-by-one candidate evaluation to a rapid, holistic mapping of chemical space to pinpoint the most promising regions for focused experimental investigation. Importantly, all shortlisted complexes are structurally documented in crystallographic databases, which enables direct experimental validation and minimizes trial-and-error in material design.