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.
Abstract
High‐entropy alloys (HEAs), with their unique surface chemical disorder and rich active site distributions, hold great promise as electrocatalysts for hydrogen evolution. However, their vast compositional space poses a fundamental challenge for rational catalyst design. In this work, we propose a Hierarchical Site to Composition Machine Learning (HS2C‐ML) framework to accelerate the discovery of efficient HEA catalysts for alkaline hydrogen evolution reaction (HER). At the site level, a fine‐tuned, site‐resolved machine‐learning model rapidly predicts *H and *OH adsorption energies across complex HEA surfaces, enabling high‐throughput activity evaluation from adsorption‐energy distributions. At the composition level, a physics‐informed sparsifying operator algorithm establishes interpretable relationships between alloy composition and catalytic performance, allowing direct screening across thousands of compositions without explicit atomistic calculations. By screening ∼16,000 HEA compositions, we computationally prioritize a series of promising HEA candidates with favorable predicted alkaline HER activities, and the relevance of the predicted composition space is supported by consistency with selected literature reports and by the synthesis and electrochemical evaluation of one representative catalyst. This work provides a general and scalable strategy for data‐driven catalyst design in high‐dimensional HEA compositional spaces.
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 NN 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.
Shangfeng Jiang, Ting Tao, Kexiang Guo et al.· ENERGY & ENVIRONMENTAL M...· 0 citations
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.
Jing Yang, Jun Yu, Jianming Cai et al.· Materials Genome Engineering...· 0 citations
Reliable prediction of hydrogen adsorption free energy (ΔGH) is essential for accelerating electrocatalyst discovery for the alkaline hydrogen evolution reaction (HER), yet practical machine-learning workflows remain limited by inconsistent energetic definitions, heterogeneous density functional theory (DFT) protocols, and poor out-of-distribution (OOD) generalization. Here, we present an energetically anchored machine-learning framework that integrates machine-learning interatomic potential (MLIP)-derived energetic descriptors with pretrained Crystal Hamiltonian Graph Neural Network (CHGNet) latent embeddings to predict DFT-defined hydrogen adsorption energetics across chemically diverse catalyst surfaces. The framework employs protocol-consistent single-point MLIP energy evaluation on reference geometries to construct thermodynamically aligned energetic descriptors while combining them with structural representations through gradient-boosting regression. Using curated data sets from Catalysis Hub and AQCat25 containing single-, bi-, and trimetallic adsorption systems, the embedding-augmented energetic model achieved high predictive accuracy (R2 = 0.976, MAE = 0.054 eV, RMSE = 0.105 eV) under site-level data splitting, substantially outperforming embedding-only and physicochemical-descriptor-only models. Shapley additive explanations analysis revealed a hierarchical learning mechanism in which the protocol-consistent energetic descriptor serves as the dominant thermodynamic anchor, whereas structural descriptors provide secondary refinements that improve adsorption-energy discrimination, particularly near the thermoneutral regime relevant to catalyst screening. Explicit evaluation of MLIP-only geometry workflows further demonstrated that the framework retains meaningful adsorption-energy ranking capability despite degradation introduced by MLIP-relaxed geometries. Additional OOD and transfer-learning analyses showed that predictive robustness depends strongly on the balance between compositional diversity and reference-protocol consistency. These results establish energetically anchored MLIP embeddings as an effective strategy for scalable post-DFT adsorption-energy refinement and data-efficient electrocatalyst screening while clarifying the practical limitations of MLIP-driven workflows for heterogeneous catalysis.
Ching-En Lin, Po-Wen Chen, Tien-Hsiang Hsueh· Journal of Chemical Informat...· 0 citations
The active sites of alloy catalysts may emerge only under reaction conditions, yet how reactive atmospheres select these sites remains unresolved. Here, we reveal how reactive atmospheres reorganize complex alloy surfaces into functional active-state distributions by using a machine-learning-potential-accelerated multiscale framework. Using selective acetylene hydrogenation on Pd–Ag alloys as a model reaction, we show that adsorbates reverse the intrinsic Ag-segregation tendency, enrich Pd in the outermost layer, and generate a distribution of Pd3 hollow ensembles with distinct second-shell coordination environments. These dynamically formed ensembles, rather than the as-prepared isolated Pd sites, govern the calculated activity and selectivity trends. Ensemble-resolved energetics and kinetics identify the adsorption free-energy gap between ethylene and acetylene as a predictive descriptor that captures the activity-selectivity trade-off and defines an optimal window balancing acetylene hydrogenation and ethylene desorption. Extending the analysis across multiple alloy families further reveals a general scaling relationship in which adsorbate-driven self-organization is governed by adsorption asymmetry and alloy stability. These results establish dynamic ensemble selection as a transferable framework for understanding and designing adaptive alloy catalysts, shifting catalyst optimization from static structural descriptors toward reaction-condition-directed control of active-state distributions.
Hong-Yue Wang, Hao Wang, Wei-Xue Li et al.· JACS Au· 0 citations
High‐entropy refractory metal silicides (HERMS) are emerging as promising candidates for ultrahigh‐temperature structural applications, generally crystallizing in either the tetragonal
C
11
b
or hexagonal
C
40 structure. The
C
11
b
phase is desirable for its higher hardness and fracture toughness, but predicting phase stability across multicomponent design spaces is computationally expensive using density functional theory (DFT). To address this, we propose a dual machine learning (ML) framework for (i) classification of stable crystal structure
C
11
b
versus
C
40, (ii) quantitative prediction of the mixing enthalpy difference (
δ
mix
), using only DFT‐free descriptors at inference. Using a dataset of 252 quaternary and quinary compositions, we demonstrate that a reduced descriptor set comprising valence electron concentration (VEC), electronegativity difference (Δ
χ
), and atomic size difference (
δ
r
) achieves high predictive accuracy for both classification and regression tasks. The classifier was validated using stratified 10‐fold cross‐validation (CV), achieving 97% balanced accuracy, a Matthews correlation coefficient (MCC) ≈ 0.96, and a receiver operating characteristic area under the curve (ROC‐AUC) ≈ 0.99, while the regression model reaches test
R
2
≈ 0.95 for
δ
mix
. This computationally efficient strategy for screening and synthesizing
C
11
b
‐stabilized HERMS compositions enables rapid exploration of multicomponent silicide design spaces, allowing researchers to reserve DFT calculations exclusively for ML‐selected candidates.
Nidhi Rawat, D. S. Bisht, P. Alagarsamy· Advanced Engineering Materia...· 0 citations
For decades, atomistic simulation of chemically complex Ni‐based superalloys has remained beyond practical reach. Here, we apply the GRACE foundational machine‐learning interatomic potential to predict chemical ordering and stacking‐fault energetics in the γ$\gamma$ and γ′$\gamma &aposx;$ phases of CMSX‐4, a commercial multicomponent Ni‐based superalloy. After benchmarking against structural and thermodynamic reference data, we use hybrid Monte‐Carlo/molecular dynamics sampling to study the impact of local chemical order on planar‐fault energies. GRACE reproduces elemental equilibrium lattice parameters within 0.50%$0.50\%$ of DFT references, while underestimating melting temperatures of ordered Ni–Al phases by up to 5.7%$5.7\%$ . The simulations reveal local chemical ordering in the γ$\gamma$ phase and the expected L12$\mathrm{L1_{2}}$ sublattice occupancies in the γ′$\gamma &aposx;$ phase. In the γ$\gamma$ phase, the short‐range order raises the shear barriers by approximately 66 mJ m−2$66~\mathrm{mJ\,m^{-2}}$ while leaving the intrinsic stacking fault energy of 28 mJ m−2$28~\mathrm{mJ\,m^{-2}}$ unchanged. In the γ′$\gamma &aposx;$ phase, alloying raises the complex and superlattice intrinsic stacking fault energies by approximately 100 mJ m−2$100~\mathrm{mJ\,m^{-2}}$ relative to stoichiometric Ni3$\mathrm{Ni_{3}}$ Al. These results show that pretrained foundational potentials enable atomistic simulations of chemically complex multicomponent superalloys at scales inaccessible to direct first‐principles calculations.
Aditya Vishwakarma, Sarath Menon, Fritz Körmann et al.· Advanced Engineering Materia...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
