Unlocking Enhanced Ionic Transport: A Machine Learning-Driven AIMD Study on Doping, Defects, and Strain in Antiperovskite Solid-State Electrolytes.

Journal: ACS applied materials & interfaces
Published Date: May 21, 2025

Abstract

Ab initio molecular dynamics (AIMD) simulations have been employed to investigate doped antiperovskite solid-state electrolyte (AP SSE) structures, specifically formulated as LiOClBr ( = 0, 0.25, 0.50, 0.75, 1). Various defects, including lithium vacancies, interstitials, as well as Schottky and Frenkel defects, were analyzed under elastic biaxial strain to simulate practical conditions. Machine learning (ML) was then applied to the data from AIMD simulations, targeting lithium diffusivity and ionic conductivity. Our results show that the most significant enhancement in lithium diffusivity occurs when the Cl/Br ratio is 0.5/0.5, and the defect type is the double lithium ion interstitial. Lithium diffusivity and conductivity are mainly governed by the vibration amplitude and number of lithium ions. Although biaxial strain offers a slight promotion effect, it is less influential compared to doping and defect structures. Additionally, SHAP analysis was conducted to assess the relative importance and interactions of each feature descriptor, offering novel insights into the design strategies for enhanced AP SSEs.

Authors

  • Chuang Lin
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Yi Dong
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.

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