Data driven computational design of stable oxygen evolution catalysts by DFT and machine learning: Promising electrocatalysts

Abstract

The revolutionary development of machine learning (ML), data science, and analytics, coupled with its application in material science, stands as a significant milestone of the scientific community over the last decade. Investigating active, stable, and cost-efficient catalysts is crucial for oxygen evolution reaction owing to the significance in a range of electrochemical energy conversion processes. In this work, we have demonstrated an efficient approach of high-throughput screening to find stable transition metal oxides under acid condition for high-performance oxygen evolution reaction (OER) catalysts through density functional theory (DFT) calculation and a machine learning algorithm. A methodology utilizing both the Materials Project database and DFT calculations was introduced to assess the acid stability under specific reaction conditions. Building upon this, OER catalytic activity of acid-stable materials was examined, highlighting potential OER catalysts that meet the required properties. We identified IrO2, Fe (SbO3)2, Co(SbO3)2, Ni(SbO3)2, FeSbO4, Fe(SbO3)4, MoWO6, TiSnO4, CoSbO4, and Ti(WO4)2 as promising catalysts, several of which have already been experimentally discovered for their robust OER performance, while others are novel for experimental exploration, thereby broadening the chemical scope for efficient OER electrocatalysts. Descriptors of the bond length of TM-O and the first ionization energy were used to unveil the OER activity origin. From the calculated results, guidance has been derived to effectively execute advanced high-throughput screenings for the discovery of catalysts with favorable properties. Furthermore, the intrinsic correlation between catalytic performance and various atomic and structural factors was elucidated using the ML algorithm. Through these approaches, we not only streamline the choice of the promising electrocatalysts but also offer insights for the design of varied catalyst models and the discovery of superior catalysts. (c) 2024 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.