Machine learning-optimized bioinspired N-doped carbon-wrapped trimetallic oxides for an efficient oxygen evolution reaction.

Machine-Learning-Guided Genetic Inverse Design of Single-Atom Electrocatalysts for CO2 Reduction

A genome-inspired materials intelligence framework (GIMI) for inverse design in high-dimensional compositional spaces is proposed, enabling targeted exploration of complex compositional space and accelerating the discovery of high-performance catalysts.

Machine learning-assisted QnSPR study of structurally diverse nanoporous carbon materials and their capacitive behavior in dilute ionic liquid electrolyte

Interpretable data-driven discovery of high-performance multielement metal oxide electrocatalysts for oxygen evolution reaction from a small hybrid dataset

ABSTRACT One of the most formidable challenges in materials chemistry is the rational design of functionalities capable of dramatically enhancing performance. However, it is well-known that the discovery of promising materials often requires several decades of continuous trial-and-error. Herein, we show an interpretable data-driven framework for the discovery of multielement metal oxide oxygen evolution reaction (OER) electrocatalysts in alkaline media within a substantially shorter timeframe. This framework was trained on a hybrid dataset comprising only 557 data, consisting of the curated literature and our own experimental results. Furthermore, this framework was specifically designed to enable extrapolative materials discovery, including the exploration of elemental combinations absent from the training database. Consequently, from a large material search space of approximately 3 million candidates, we identified a promising unconventional quinary oxide composed of V, Ni, W, Rh, and Ru that exhibits, in 0.1 M KOH, an exchange current density approximately 20 times higher than that of IrO2. This work serves as a proof-of-concept, demonstrating that the rational design of high-performance electrochemical functionalities from an extensive candidate space can be achieved using a small hybrid dataset combined with an interpretable data-driven approach.

Machine-Learning-Assisted First-Principles Approach for the Electrochemical Reduction of CO2 on Single-Atom Catalysts

Molecular design of an active electrocatalyst can determine the primary product. Precise selection of the active metal center influences the adsorption of reactants, intermediates, and product selectivity and ultimately affects the efficiency of the process. Here, we predict a potent catalyst, namely, Fe@C6N6, with enhanced activity and low overpotential for CO2 reduction reactions (CO2RRs) utilizing the density functional theory (DFT) approach, coupled with machine-learning-based model building. A series of 24 transition-metal single-atom catalysts (SACs) anchored on the C6N6 monolayer were screened, among which 19 exhibit effective CO2 activation. Employing density functional theory (DFT), a systematic study on reaction pathways demonstrates that 13 catalysts favor CH4 formation, while the remaining 6 exhibit selectivity toward CH3OH production. Additionally, 9 among the 19 catalysts are further screened out as they preferentially promote the competing hydrogen evolution reaction (HER). The active SACs demonstrate strong CO2RR activity, with Fe@C6N6 emerging as the most promising catalyst with the lowest limiting potential (0.39 V). To further establish atomic–property relationships, a machine-learning (ML) regression model was developed to predict adsorption energies using statistically significant descriptors and identify key factors governing CO2 adsorption. Furthermore, a classification model developed using the elemental characteristics of the metal atoms shows excellent correlation with the DFT results across the dataset. This combined DFT and machine-learning approach provides mechanistic insights for the rational development of efficient SACs for CO2 electroreduction.

Interpretable Machine Learning Framework Deciphers the Role of Local Environment of High‐Entropy Intermetallic Compounds for Alkaline Hydrogen Evolution Reaction

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.

Machine-Learning-Guided Design of Pd–Au Electrocatalysts for the Oxygen Reduction Reaction

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.