Machine learning enabled rational design of atomic catalysts for electrochemical reactions

Abstract

Atomic catalysts (ACs) with ultimate atomic utilization and unique catalytic properties have emerged as promising candidates for high-performance catalysts because of their great potential for enabling the efficient use of...

Rational design of single transition-metal atoms anchored on a PtSe2 monolayer as bifunctional OER/ORR electrocatalysts: a defect chemistry and machine learning study

“…The machine learning (ML) model to unveil data correlations in materials science is gaining popularity. 94–97 In our study, we utilize ML models to investigate further the inherent connection between adsorption energy and various structural and atomic properties, as illustrated in Fig. 5(a).…”

Computational chemistry for water-splitting electrocatalysis

“…It enables the exploration of relationships that are challenging to definitively and precisely model mathematically, proving highly valuable in the de novo discovery of important physical and chemical principles. 143–145…”