Aug 2026· ChemistrySelect· 0 citations· 194 references
TL;DR
A comprehensive account of ML applications in NRR, covering curated experimental databases, feature engineering based on atomic, structural, and DFT‐derived descriptors, and ML‐guided insights into single‐atom, dual‐atom, alloy, oxide, nitride, and defect‐engineered catalysts are presented.
Abstract
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.
With growing concerns over climate change, electrochemical CO2 reduction (CO2RR) has attracted significant attention for converting CO2 into valuable products under mild conditions, but catalyst design remains challenging because traditional, experiment-driven approaches are inefficient and time-consuming. Recently, machine learning (ML), as an advanced data-driven technology, has been widely applied to catalyst design and performance prediction, providing new approaches for rapid screening, structural optimization, and mechanistic exploration of catalysts. Building on these recent advances, this review summarizes the latest applications of ML in CO2RR technology, focusing on ML applications in catalyst performance prediction, key descriptor discovery, catalyst screening, mechanistic investigation, and optimization of synthesis parameters. Additionally, this review discusses the challenges faced by ML in CO2RR catalyst development, including data scarcity, poor model generalization, and difficulties in multiobjective optimization, while looking ahead to future directions such as transfer learning, the establishment of automated synthesis platforms, and the integration of multiobjective optimization frameworks, thereby providing a roadmap for the rational design of next-generation CO2RR catalysts that aims to guide the development of data-driven catalyst design approach.
The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial data set of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental data set. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multielemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1)/CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt %), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency.
Pengfei Du, Abdellah Ait El Fakir, S. Mine et al.· Journal of the American Chem...· 0 citations
Photocatalytic water splitting has garnered immense interest as a sustainable pathway for clean hydrogen production by directly converting solar energy into chemical fuel. However, challenges related to intricate charge carrier dynamics, surface redox kinetics, and the vast search space for multicomponent catalysts continue to constrain the systematic development of efficient systems. Machine learning (ML) has emerged as a transformative tool to address these bottlenecks by enabling the accurate prediction of electronic properties, the identification of promising heterostructures, and the optimization of reaction conditions while reducing reliance on traditional trial‐and‐error methods. By capturing complex nonlinear correlations among structural descriptors and catalytic performance, ML facilitates the exploration of high‐dimensional design spaces that are essential for advancing solar‐to‐fuel conversion research. This review provides a comprehensive overview of how ML supports systematic materials innovation to realize stable and high‐efficiency systems for sustainable hydrogen evolution. As such, the integration of ML with experimental and theoretical methodologies is expected to establish a predictive and systematic framework for photocatalyst development, thereby accelerating progress toward scalable solar‐to‐hydrogen energy conversion.
Heesung Yoon, Jin Hyuk Cho, Wee‐Jun Ong et al.· ChemPhotoChem· 0 citations
Selective conversion of nitrogen-containing species into harmless molecular nitrogen (N2) remains a key challenge in the catalytic oxidation of nitrogen-containing volatile organic compounds (NVOCs). Machine learning (ML) provides an effective approach for predicting catalytic performance and identifying key descriptors from complex literature-derived datasets. Herein, a literature-derived catalyst database was constructed to predict N2 selectivity during NVOC oxidation and clarify the factors governing nitrogen transformation. Thirteen descriptors related to catalyst composition, structural properties, support acidity, and reaction conditions were used to train eight ML models. Among them, the ExtraTrees model exhibited the best predictive performance, with a coefficient of determination of 0.958 and a root mean square error of 7.638 on the test set. Shapley additive explanations and partial dependence plots revealed that oxygen concentration, reactant concentration, reaction temperature, gas hourly space velocity, and support acidity were the dominant factors affecting N2 selectivity, with support acidity identified as the key catalyst-related descriptor. Guided by this descriptor-level insight, Cu/M and CuFe/M catalysts (M = SiO2, ZSM-5, and Al2O3) were prepared and evaluated for acetonitrile oxidation. The catalytic and spectroscopic results confirmed the predicted role of support acidity, showing that different supports regulate CH3CN adsorption, CN activation, and the evolution of hydrolysis and oxidation related nitrogen-containing intermediates, thereby affecting nitrogen-product distributions and N2 selectivity. This work integrates interpretable machine-learning prediction with targeted external validation and mechanistic analysis, providing mechanistic insight into support-acidity-regulated nitrogen transformation and guidance for designing NVOC oxidation catalysts with high N2 selectivity.
Haotian Hu, Ying Wang, Zihao Zhai et al.· Journal of Colloid and Inter...· 1 citation
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 study demonstrates an example of how machine-learning-driven materials discovery can accelerate catalyst design while simultaneously offering an experimental solution to the persistent challenge of high ORR overpotential.
Darik A. Rosser, Anto Felix Sotvik GS, Kevin C. Leonard· ACS Applied Energy Materials· 0 citations
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