Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Single Pd-doped arsenene coordinated with nitrogen atoms as an electrocatalyst for effective chlorine evolution reaction: DFT and machine learning studies

We designed a series of single transition metal-anchored arsenene coordinated with nitrogen atoms (TMNx@As) as electrocatalysts for chlorine evolution reaction (CER). Density functional theory (DFT) and machine learning were employed to investigate the catalytic activity of TMNx@As. It is found that the performance of TMNx@As is the best when the transition metal is Pd and the nitrogen coordination content is 66.67%. The catalytic activity of TMNx@As for chlorine evolution reaction is mainly determined by the covalent radius (Rc) and atomic non-bonded radius (Ra) of the transition metal and the fraction of N atoms in metal's coordinating atoms (fN).

Machine Learning Descriptors for Data-Driven Catalysis Study

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane

Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...