Skip to content
Log in

Similar papers

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 potential is used for machine-learning molecular dynamics (MLMD), combined with surface DFT calculations at the PBE level. Two-phase MLMD simulations identify an equilibrium melting temperature of approximately 1350 K, while single-phase heating of a defect-free crystal yields an apparent transition at 1700K, interpreted as a superheating-limited upper bound. Within this solid-phase temperature window, DFT calculations show that atomic H is strongly chemisorbed on Co (Eads = −2.04 eV) and O (Eads = −4.29 eV) sites, with O-H bond lengths consistent with experiment, while molecular H₂ is only weakly physisorbed (Eads = 0.001 eV). The recombinative desorption of two co-adsorbed H atoms on adjacent Co/O sites proceeds with an activation barrier of 0.46 eV and an estimated rate of 1.6 × 1010 s−1 at 1300 K, confirming that H-H recombination is fast and is not the rate-limiting step under hydrogen-rich conditions. All DFT values are reported without an explicit Hubbard U correction and represent best feasible estimates within the present computational constraints.

R. Arifin, Y. Winardi, I. Widaningrum et al. · 0 citations
Preprint 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 for hydrogen release. For pristine MgH2, our simulations revealed a novel H2 formation mechanism characterized by H2 generation in the subsurface region followed by diffusion to the surface for desorption, highlighting the critical role of subsurface processes beyond conventional surface-driven pathways. Comprehensive screening of 22 doping elements identified Ni as the most effective dopant. Among several descriptors, machine learning analysis identified the time-coupled Miedema electron density as the critical descriptor, underscoring the role of electronic properties. Consequently, a volcano-shaped relationship was uncovered between the intrinsic Miedema electron density ( nws ) and total hydrogen release (optimal window: 4.0<nws<5.4x10-2 e/bohr3). Dopants within this range serve a dual function: acting as thermodynamic sinks for H attraction while maintaining a balanced interaction strength to facilitate H-H coupling and H2 release. This atomistic-level validation provides strong theoretical support for the experimentally observed"hydrogen pump"effect of catalytic phases. The present study demonstrates the strong capability of ML- MD in navigating through complex catalytic mechanisms and establishing quantitative property-activity relationships, providing a robust framework for rational design of high-performance catalysts for MgH2 and other hydrogen storage materials.

Bo Han, Jianchuan Wang, Rui Zhang et al. · 0 citations
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.

Chen Zhu, H. Yang, Haifeng Wang et al. · 0 citations
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‐performance HER catalysts from a huge HEI material space (10 6 ) based on our newly established machine learning (ML) driven “decode—describe—design” (3D) framework by experimentally fabricated A 3 B‐type (FeCoNi) 3 (AlTi) system. Over 700 catalysts exhibited better performance than existing experimental results, indicating that the experiment only touched a very small part of the material space. Moreover, we developed various powerful descriptors (such as Λ OH , Λ H ) and analysis tools (such as, RDERA, RPDAD, CPMCC, CPMCS) to decouple the complex interplay of elements into atomic‐ and region‐specific effects, laying the foundation for the establishment of structure‐activity relationships and guiding the rational design of catalysts. The interpretable ML‐driven 3D framework, powerful descriptors, and novel analysis tools enable efficient design and screening, catalytic mechanism elucidation, and structure‐activity relationship establishment. They are expected to stimulate further computational and experimental investigations in related catalyst systems.

Hao Deng, Liming Yang · 0 citations
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 configurational entropy to stabilize single-phase solid solutions, while surface heterogeneity creates new catalytic motifs in which collective electronic and geometric effects yield activities exceeding those of pure metals. These attributes make HEAs particularly promising for the hydrogen evolution reaction (HER). Platinum-group-metal-containing HEAs (PGM-HEAs) exhibit exceptional HER activity and durability in acidic environments; however, their reliance on multiple precious metals limits large-scale deployment. Recent efforts demonstrate that partial substitution with earth-abundant transition metals can significantly reduce noble-metal content without sacrificing performance. Despite this progress, the atomic-scale origins of HER activity in HEAs—specifically the geometric and electronic descriptors governing optimal hydrogen binding—remain insufficiently understood. Here, we present an integrated density functional theory (DFT) and machine learning (ML) framework for discovering PGM-lean HEAs optimized for HER. Equimolar binary and ternary alloys are constructed from a nine-metal design space (Pt, Pd, Ir, Rh, Fe, Co, Ni, Mo, W), yielding 120 unique compositions. Representative surface configurations are selected using Kennard–Stone sampling and modeled as close-packed FCC(111) and BCC(110) slabs. Hydrogen adsorption energetics, and local active-site descriptors are computed using DFT and analyzed using ML models to establish structure–property relationships. Benchmark calculations on elemental FCC(111) surfaces reproduce expected periodic trends, with hydrogen adsorption free energies ranging from −0.46 eV on Ni(111) to −0.24 eV on Pt(111) at 1/4 monolayer coverage, consistent with d-band theory. Coverage effects are quantified by comparing 2×2×7 and 3×3×7 slab models, revealing stabilization of H* by approximately 0.02 eV at lower coverage. These results validate the computational methodology and establish a robust foundation for screening multicomponent HEA surfaces. The combined DFT–ML framework enables the rational identification of cost-effective, high-performance HEA electrocatalysts while providing fundamental insight into active-site chemistry in complex alloy systems. All computational data will be made available upon request to promote transparency and reproducibility.

Matthew R. Curry, Abdennaceur Karoui, Bijandra Kumar · 0 citations
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 use in catalyst screening. Here, using extended phthalocyanines (M1M2-ePc), we establish an integrated DFT-ML-experiment framework that maps catalytic performance onto an interpretable electronic landscape. Screening 81 DFT-computed and 360 ML-predicted metal pairs identifies FeM-ePc as a promising bifunctional catalyst family. Notably, SHapley Additive exPlanations (SHAP) analysis highlights the importance of electronic background and the key role of the secondary metal in regulating catalytic activity. First-principles calculations further uncover a cooperative dual-descriptor mechanism, in which d-band center and charge transfer jointly govern bifunctional activity. Combining LASSO with SISSO yields compact analytical formulas that quantitatively reproduce ηORR and ηOER, providing interpretable descriptors for DACs. Guided by these findings, FeCo-ePc-L with atomically dispersed Fe-Co sites was synthesized to experimentally examine the ML-guided prediction. This work highlights the utility of interpretable ML for mechanistic discovery in DACs by revealing role-asymmetric electronic cooperation between paired metal centers.

Shao-Bo Jia, Lu Yang, Chou Wu et al. · 0 citations