Two‐Stage Material Screening (TSMS) is developed, an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts.
Abstract
Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi‐physics nature of ORR/OER—together with diverse operating environments—poses significant challenges to the rational design of electrocatalysts based on structure–property relationships. To overcome these challenges, we developed Two‐Stage Material Screening (TSMS), an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts. Demonstrated in protonic solid oxide cells (P‐SOCs), TSMS screened 6,940,032 compositions and identified top‐performing candidates that were experimentally validated, achieving a peak power density of 2.68 W cm−2 in fuel cell mode and a current density of 3.51 A cm−2 at 1.3 V in electrolysis mode, with stable performance maintained over 500 h at 600°C. Our analysis revealed that electron affinity is strongly associated with thermodynamic stability, while d‐p hybridization and densification resistance emerge as the primary descriptors governing electrocatalytic activity. By combining predictive modeling with mechanistic understanding, TSMS establishes a versatile and broadly generalizable paradigm for accelerating materials discovery.
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations
Anion exchange membrane water electrolyzers (AEMWEs) are promising for hydrogen production, yet their performance is bottlenecked by the alkaline hydrogen evolution reaction (HER) with sluggish kinetics induced by high water dissociation barriers and imbalanced H*/OH* adsorption-desorption. Herein, interpretable machine learning (ML) is exploited as a core tool for precise catalyst structure optimization, guiding the fabrication of a Ru2Ni3-carbon nanotubes (CNTs) hybrid catalyst. The ML-engineered catalyst exhibits high HER activity, with an ultra-low overpotential of 14 mV at 10 mA cm–2 and a Tafel slope of 34.3 mV dec–1. When integrated into an AEMWE with a NiFe-LDH anode, the system achieves 1.86 V at 1 A cm–2 (80 °C, no iR correction) and maintains stability for 200 h. Experimental and theoretical studies confirm that the ML-tailored Ru2Ni3-CNTs synergy modulates d-band centers, reduces reaction barriers, and optimizes intermediate adsorption, highlighting ML’s pivotal role in rational electrocatalyst design for advanced AEMWEs.
Hydrogen (H2) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H2 remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H2 storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H2 adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H2 uptake. This offers engineers and researchers a practical means of screening potential MOFs for H2 storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts.
R. Taylor, Shahin Alipour Bonab, M. Yazdani-Asrami· Algorithms· 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
The transition toward carbon-neutral chemical manufacturing requires catalytic systems that can convert abundant waste molecules into value-added chemicals under mild, renewable-energy-driven conditions. Single-atom electrocatalysts (SAECs), featuring isolated metal centers anchored on tailored supports, offer maximum atom utilization, tunable coordination environments, and well-defined active sites for complex multielectron reactions. This review critically examines the emerging role of artificial intelligence in accelerating the discovery, optimization, and mechanistic understanding of SAECs for carbon dioxide electroreduction and green ammonia synthesis through nitrate conversion. First, fundamental design principles are discussed, including metal–support interactions, coordination engineering, electronic-structure modulation, and stability limitations. Next, machine learning, density functional theory integration, high-throughput screening, explainable descriptors, and self-driving laboratory concepts are evaluated as tools for rational catalyst development. Particular emphasis is placed on CO₂ valorization pathways toward CO, formate, hydrocarbons, and alcohols, together with nitrate-to-ammonia conversion as a sustainable nitrogen-recycling strategy. Advanced in situ/operando characterization, performance benchmarking, selectivity control, and degradation mechanisms are also assessed. Finally, this review highlights current barriers related to data quality, catalyst durability, reactor design, product separation, techno-economic feasibility, and industrial scale-up. The article provides a forward-looking framework for integrating AI, atomically precise catalysis, and renewable electrosynthesis in future sustainable chemical manufacturing.
Swaira Anjum, Amir Sohail, Muhammad Ibrahim et al.· Scholars International Journ...· 0 citations
Machine learning has significantly reduced the computational power necessary to estimate the free energy of adsorption of key reaction intermediates on a diverse range of catalytic surfaces. Nevertheless, translating this computational capability into the discovery and experimental validation of catalysts necessitates targeting specific questions where this faster computation can yield the most significant impact. Using the electrochemical oxygen reduction reaction (ORR) as a model reaction due to its known linear scaling relationships between key catalytic intermediates, we demonstrate that the Open Catalyst Project’s machine-learning-based calculations of adsorption energies can inform experimental catalytic research. The primary challenge we addressed was the structural effect of the ORR, wherein the higher Miller index facets of Pt exhibit diminished ORR activity in comparison to Pt(111). The Open Catalyst Project’s rapid relaxation energy calculations enabled us to screen a large number of bimetallic materials for each key intermediate of the ORR over a wide range of crystal facets. The Open Catalyst Project was able to identify that PdAu3 can overcome the negative structural effects observed on the higher Miller index facets of Pt for the ORR. Electrochemical experimentation via rotating disk electrode linear sweep voltammetry and Tafel slope analysis revealed that polycrystalline PdAu3 nanoparticles exhibit an improved onset potential for the ORR compared to commercial polycrystalline Pt/C. Thus, 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