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Solid electrolytes (SEs) are crucial materials to realize highly safe and practical all-solid-state Li+-ion batteries. Here, we performed a large-scale computational SE screening on a chemical space of >10 000 Li-rich inverse-perovskite (ip) compounds with tetragonal and cubic structures by high-throughput density functional theory (DFT) and AI-driven methods. A total of 1413 novel candidate compounds were predicted to be synthesizable based on thermodynamic decomposition energy (Ed) and machine-learned experimental synthesis likelihood (Ls). These compounds were further screened using a Pareto-front approximation set of a multiobjective Bayesian optimization tasks for k = 3 DFT-calculated SE properties (fk, with k = 1, 2, and 3): (i) electrochemical window from electronic band gap energy (f1: Eg), (ii) chemical stability by reaction with moisture (f2: Eh), and (iii) 400 K bulk Li+-ion conductivity (f3: Λ). As a result, the compound list was reduced down to 24 candidate ip SEs, and examples include Cm Li8O2Cl3Br (Ed = 0, Ls > 0.5, Eg = 4.74 eV, Eh = −33.22 kJ/mol, and Λ = 9.0 × 10–4 S/cm), Amm2 Li8OSCl4 (Ed = 0.070 eV/atom, Ls > 0.5, Eg = 4.14 eV, Eh = −40.70 kJ/mol, and Λ = 9.2 × 10–2 S/cm), and Cmcm Li12O3SeClBr3 (Ed = 0.097 eV/atom, Ls > 0.5, Eg = 3.36 eV, Eh = −86.88 kJ/mol, and Λ = 7.8 × 10–1 S/cm). Possible solid-state synthesis routes for the screened SE candidates were also explored using thermodynamic phase competition analysis and classical nucleation theory reaction barrier. Aside from providing a well-informed list of potentially novel ip-type SEs, our work also reports on an effective calculation methodology for tiered large-scale material screening which, at the same time, incorporates “small data” learning on target property datasets that are computationally expensive to obtain. The generated datasets are expected as well to be of great utility for future data-driven material design efforts.

権利情報:

• In Copyright
  This document is the Accepted Manuscript version of a Published Work that appeared in final form in The Journal of Physical Chemistry C, copyright © 2023 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jpcc.3c02801

キーワード: all solid state batteries, solid electrolytes, density functional theory, materials informatics, machine learning, novel materials search

刊行年月日: 2023-09-07

出版者: ACS Publications

掲載誌:

• Journal of Physical Chemistry C (ISSN: 19327455) vol. 127 issue. 35 p. 17307-17323

研究助成金:

• JST ALCA-SPRING JPMJAL1301 (JST, Japan Science and Technology Agency)
• JST COI-NEXT JPMJPF2016 (JST, Japan Science and Technology Agency)
• JSPS KAKENHI JP19H05815 (JSPS, Japan Society for the Promotion of Science)
• JSPS KAKENHI JP21K14729 (JSPS, Japan Society for the Promotion of Science)
• Materials Processing Science project (Materealize) JPMXP0219207397 (MEXT)
• Program for Promoting Researchon the Supercomputer Fugaku JPMXP1020200301 (MEXT)

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4305

公開URL: https://doi.org/10.1021/acs.jpcc.3c02801

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更新時刻: 2024-08-28 08:30:15 +0900

MDRでの公開時刻: 2024-08-28 08:30:16 +0900