Aug 2026· ENERGY & ENVIRONMENTAL MATERIALS· 0 citations· 62 references
Abstract
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.
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.
Shiyu Zhen, Lingwei Wang, Jinhao Wang et al.· Advanced Functional Material...· 0 citations
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· Advanced Energy Materials· 0 citations
This analysis reveals an intrinsic stability-performance trade-off, where the disorder required to maximise the transformation driving force inevitably penalises thermodynamic stability, and proposes a hierarchical tuning strategy that decouples phase transition temperature from hysteresis, providing a scalable paradigm to transform HEA exploration from serendipitous discovery into rational design.
Zhe Cui, C. Romero-Muñiz, J. Law et al.· Nature Communications· 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
This work presents a novel approach toward high‐energy molecules by combining long short‐term memory (LSTM) networks for molecular generation and attentive graph neural networks (GNN) for property predictions by combining fixed SHA‐256 embeddings with partially trainable representations.
Siddharth Verma, A. Alankar· Propellants, explosives, pyr...· 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
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