摘要
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 co nversion 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 IrO_(2),Fe(SbO_(3))_(2),Co(SbO_(3))_(2),Ni(SbO_(3))_(2),FeSbO_(4),Fe(SbO_(3))4,MoWO_(6),TiSnO_(4),CoSbO_(4),and Ti(WO_(4))_(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.
基金
supported by the Soonchunhyang University Research Fund
supported by the Supercomputing Center/Korea Institute of Science and Technology Information with supercomputing resources(KSC-2022-CRE-0354)
supported by the “Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004)
a study on the“Leaders in INdustry-university Cooperation 3.0”Project,supported by the Ministry of Education and National Research Foundation of Korea
funded by BK 21 FOUR(Fostering Outstanding Universities for Research)(5199991614564)
supported by the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(CRC-20-01-NFRI)
supported by the research fund of Hanyang University(HY-2022-3095)
supported by the Technology Innovation Program(20023140,Development of an integrated low-power,highperformance,cryogenic high-vacuum exhaust system for analyzing impurity concentrations in the process in real time)funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea)。
