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.
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.
Shiyu Zhen, Lingwei Wang, Jinhao Wang et al.· Advanced Functional Material...· 0 citations
The electrochemical nitrogen reduction reaction (NRR) offers a promising route for sustainable ammonia synthesis under ambient conditions. Yet, its practical development remains constrained by low catalytic activity, poor selectivity against the competing hydrogen evolution reaction (HER), and the limited availability of rigorously validated experimental data. In recent years, machine learning (ML) has emerged as a powerful tool for accelerating NRR catalyst discovery by mining heterogeneous literature datasets, revealing structure–property relationships, and identifying unexplored catalyst electrolyte operating windows. This review presents a comprehensive account of ML applications in NRR, covering curated experimental databases, feature engineering based on atomic, structural, and DFT‐derived descriptors. Particular emphasis is placed on ML‐guided insights into single‐atom, dual‐atom, alloy, oxide, nitride, and defect‐engineered catalysts. Descriptor‐driven design rules are used to identify promising catalyst motifs through DFT screening and gradient‐boosted regression. The review critically examines the limitations of current ML‐assisted NRR research, including data scarcity, and the mechanistic overlap between NRR and HER. In this context, uncertainty quantification and interpretability tools such as SHAP analysis and partial dependence plots are highlighted as valuable strategies for improving model reliability. Finally, emerging closed‐loop DFT‐ML‐experiment workflows, active learning, and ML models are discussed as essential pathways toward practical, scalable ammonia electrosynthesis.
Rationally selection of precursor additives during perovskite crystallization is essential for obtaining high‐quality films and achieving high‐performance perovskite solar cells (PSCs). However, the discovery and optimization of effective additives still rely heavily on time‐consuming and costly trial‐and‐error experiments. In this work, we proposed a machine learning (ML) assisted screening strategy for precursor additives by integrating process parameters, material physicochemical properties, and molecular descriptors into a unified feature system. Among the five ML algorithms evaluated, the random forest model achieved the best predictive performance with relative errors below 5% on the external dataset. SHapley Additive exPlanations (SHAP) analysis further quantified the contribution of key features to device efficiency, offering guidance for additive structural design and property optimization. Following the established prescreening rules, two additives, [1,2,4]triazolo[1,5‐
a
]pyridine‐6‐carboxylic acid (6‐CATPy) and 4‐hydroxybenzenesulfonamide (4‐HBSA), were selected for experimental closed‐loop verification. Both additives effectively improved the preferred crystallization orientation, surface morphology, and defect passivation of perovskite films, resulting in enhanced film quality and power conversion efficiencies (PCEs) of 24.07% and 25.44%, respectively. These findings validate the proposed data‐driven screening strategy and demonstrate its potential for accelerating the rational discovery and optimization of precursor additives for high‐performance PSCs.
Zhimin Feng, Kuo Wang, Di Huang et al.· Rare Metals· 0 citations
Developing efficient nickel–iron-based Oxygen Evolution Reaction (OER) catalysts via plasma-assisted electrodeposition holds great promise for the green hydrogen economy due to its compatibility with large-scale industrial manufacturing. However, the vast catalyst design space remains largely unexplored due to the inefficiency of traditional trial-and-error investigation. Herein, we develop a Machine Learning-guided Genetic Algorithm (ML-GA) paradigm with two complementary modes: exploration and exploitation. The exploration mode prioritizes population diversity while maintaining promising predicted performance to broadly sample the chemical space, while the exploitation mode focuses on high-performance regions to identify promising catalysts. Guided by this framework, NiFe/NiS catalysts were prioritized and synthesized via plasma-assisted electrodeposition, which demonstrated remarkable OER activity with low overpotentials of 219 mV and 315 mV at current densities of 10 mA cm
−2
and 1000 mA cm
−2
, respectively. This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts.
Yina Guo, Yansong Zhou, Zhuming Mao et al.· Plasma Science and Technolog...· 0 citations
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· ECS Meeting Abstracts· 0 citations
Machine learning (ML) demonstrates profound potential to accelerate the development of halide solid‐state electrolytes (HSSEs) for all‐solid‐state lithium batteries (ASSLBs). Further synergistic integration of ML with theoretical computational methods enables researchers to effectively decipher complex structure–property relationships, predict essential performance metrics, and guide rational design of high‐performance HSSEs. This review begins with a comprehensive overview of HSSEs, emphasizing their structural characteristics, ion transport mechanisms, and prevailing challenges. The theoretical basis and advanced computational techniques are then systematically elaborated along with their specific roles in simulating ion migration behavior, thermodynamic stability, and interfacial phenomena. The core of the review focuses on the integration of diverse ML approaches—encompassing supervised, semi‐supervised, unsupervised, and reinforcement learning—combined with theoretical methods to facilitate high‐throughput screening, feature engineering, property prediction, and mechanistic interpretation. Representative applications are elaborated, including the prediction of ionic conductivity, migration energy barriers, activation energy, and electrochemical stability, as well as the development of machine learning potentials for realistic multiscale simulations. Finally, the review provides forward‐looking perspectives on emerging research paradigms and further expansion beyond lithium‐based systems. This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.
Jiahui Ye, Ming Gao, Minyu Jia et al.· Advanced Functional Material...· 0 citations