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Jul 2026
Machine-learning molecular dynamics and DFT analysis of CoFe2O4 spinel stability and hydrogen recombination energetics for high-temperature thermochemical hydrogen production
ABSTRACT This study examines the structural stability of CoFe2O4 and the energetics of Langmuir–Hinshelwood recombinative desorption of two pre-adsorbed hydrogen atoms on the CoFe2O4 (111) surface, relevant to high-temperature thermochemical hydrogen production. A pretrained universal M3GNet graph neural-network potent...
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· Molecular Simulation · 0 citations
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Jul 2026
Machine Learning Potential-Driven Molecular Dynamics Simulations of Dehydrogenation in Pristine and Doped MgH$_2$
Machine learning potential-driven molecular dynamics simulations (ML-MD) were employed to provide atomistic insights into the dehydrogenation kinetics of pristine and doped MgH2. Through systematic investigation of distinct surface orientations, the MgH2 (100) surface was identified as the most active low-index surface...
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Jul 2026
Machine-Learning-Guided Genetic Inverse Design of Single-Atom Electrocatalysts for CO2 Reduction
A genome-inspired materials intelligence framework (GIMI) for inverse design in high-dimensional compositional spaces is proposed, enabling targeted exploration of complex compositional space and accelerating the discovery of high-performance catalysts.
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Aug 2026
Interpretable Machine Learning Framework Deciphers the Role of Local Environment of High‐Entropy Intermetallic Compounds for Alkaline Hydrogen Evolution Reaction
The application of high‐entropy intermetallic (HEI) compounds in the field of catalysis has attracted widespread attention, but their huge material space seriously hinders experimental exploration. Herein, we for the first time reported the efficient design, screening, and prediction of a great deal of high‐perform...
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Jul 2026
Machine Learning-Guided Discovery of PGM-Lean High-Entropy Alloys for Efficient Solar Hydrogen Evolution
High-entropy alloys (HEAs) have emerged as a powerful materials platform for electrocatalysis due to their tunable surface energetics, structural stability, and diverse local atomic environments arising from multielement interactions. Composed of several principal elements in near-equimolar ratios, HEAs leverage high c...
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Open access
Aug 2026
Machine Learning Reveals Role-Asymmetric Electronic Cooperation in Fe-Based Dual-Atom Catalysts.
Dual-atom catalysts (DACs) provide a powerful platform for oxygen electrocatalysis, yet rational design remains limited by the lack of transferable mechanistic principles. Machine learning (ML) has the potential to address this gap, yet its role in mechanistic discovery remains largely underexplored despite its wide us...
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