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

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Journal of Energy Chemistry 2024年第4期91卷 645-655页

作者： Hwanyeol Park yunseok Kim Seulwon choi Ho Jun Kim Department of Display Materials Engineering Soonchunhyang UniversityAsanChung-nam 31538Republic of Korea Department of Electronic Materials Devicesand Equipment EngineeringSoonchunhyang Uni-versityAsanChungnam 31538Republic of Korea Department of Mechanical Engineering Hanyang University ERICA Campus55 Hanyangdaehak-roSangnok-guAnsanGyeonggi-do 15588Republic of Korea

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 *** active,stable,and cost-efficient catalysts is crucial for oxygen evolution reaction owing to the significance in a range of electrochemical energy co nversion *** 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 *** upon this,OER catalytic activity of acid-stable materials was examined,highlighting potential OER catalysts that meet the required *** 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 *** of the bond length of TM-O and the first ionization energy were used to unveil the OER activity *** the calculated results,guidance has been derived to effectively execute advanced high-throughput screenings for the discovery of catalysts with favorable ***,the intrinsic correlation between catalytic performance and various atomic and structural factors was elucidated using the ML *** 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 c

关键词： Transition metal oxides Oxygen evolution reaction High-throughput screening First-principles calculation Machine learning

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A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

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能源与人工智能(英文) 2021年第3期5卷 141-149页

作者： Vijay Mohan Nagulapati Hyunjun Lee DaWoon Jung SalaiSargunan S Paramanantham Boris Brigljevic yunseok choi Hankwon Lim School of Energy and Chemical Engineering UNISTUlsanRepublic of Korea Xynergypower Corp Gyeonggi-doRepublic of Korea Department of Energy Engineering UNISTUlsanRepublic of Korea

To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost ***,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode ***,there is significant effect of the operating conditions on the battery *** machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely *** this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of ***-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE *** prediction accuracy is further compared with re-spect to single sensor and multi sensor ***,a combined multi battery data set model is used to improve the prediction *** the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.

关键词： Capacity estimation State of health Gaussian process regression Support vector machine Lithium ion battery

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