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

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

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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 exam-ined,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 perfor-mance,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 cat-alyst models and the discovery of superior catalysts.

Transition metal oxidesOxygen evolution reactionHigh-throughput screeningFirst-principles calculationMachine learning

Hwanyeol Park、Yunseok Kim、Seulwon Choi、Ho Jun Kim

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Department of Display Materials Engineering,Soonchunhyang University,Asan,Chung-nam 31538,Republic of Korea

Department of Electronic Materials,Devices,and Equipment Engineering,Soonchunhyang Uni-versity,Asan,Chungnam 31538,Republic of Korea

Department of Mechanical Engineering,Hanyang University ERICA Campus,55 Hanyangdaehak-ro,Sangnok-gu,Ansan,Gyeonggi-do 15588,Republic of Korea

Soonchunhyang University Research FundSupercomputing Center/Korea Institute of Science and Technology Information with supercomputing resourcesthe"Regional Innovation Strategy(RIS)"through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(the"Leaders in INdustryuniversity Cooperation 3.0"ProjectMinistry of Education and National Research Foundation of KoreaBK 21 FOUR(Fostering Outstanding Universities for Research)National Research Council of Science and Technology(NST)grant by the Korea government(MSIT)research fund of Hanyang UniversityTechnology Innovation Program

KSC-2022-CRE-03542021RIS-0045199991614564CRC-20-01-NFRIHY-2022-309520023140

2024

10.1016/j.jechem.2023.12.048

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.91(4)
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