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Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery

Sun, Qintao and Xiang, Yan and Liu, Yue and Xu, Liang and Leng, Tianle and Ye, Yifan and Fortunelli, Alessandro and Goddard, William A., III and Cheng, Tao (2022) Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery. Journal of Physical Chemistry Letters, 13 (34). pp. 8047-8054. ISSN 1948-7185. doi:10.1021/acs.jpclett.2c02222. https://resolver.caltech.edu/CaltechAUTHORS:20221012-041032603

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Abstract

X-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/acs.jpclett.2c02222DOIArticle
ORCID:
AuthorORCID
Fortunelli, Alessandro0000-0001-5337-4450
Goddard, William A., III0000-0003-0097-5716
Cheng, Tao0000-0003-4830-177X
Additional Information:T.C. thanks the Collaborative Innovation Center of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, the National Natural Science Foundation of China (21903058 and 22173066), the Natural Science Foundation of Jiangsu Higher Education Institutions (SBK20190810), and the Jiangsu Province High-Level Talents (JNHB-106) for support.
Funders:
Funding AgencyGrant Number
Collaborative Innovation Center of Suzhou Nano Science and TechnologyUNSPECIFIED
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)UNSPECIFIED
111 ProjectUNSPECIFIED
Joint International Research Laboratory of Carbon-Based Functional Materials and DevicesUNSPECIFIED
National Natural Science Foundation of China21903058
National Natural Science Foundation of China22173066
Natural Science Foundation of Jiangsu Higher Education InstitutionsSBK20190810
Jiangsu Province High-Level TalentsJNHB-106
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1538
Issue or Number:34
DOI:10.1021/acs.jpclett.2c02222
Record Number:CaltechAUTHORS:20221012-041032603
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20221012-041032603
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117363
Collection:CaltechAUTHORS
Deposited By: Donna Wrublewski
Deposited On:12 Oct 2022 22:41
Last Modified:12 Oct 2022 22:41

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