Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Author:

Li Jin1ORCID,Zhou Meisa2,Wu Hong‐Hui23ORCID,Wang Lifei4,Zhang Jian5,Wu Naiteng1,Pan Kunming6,Liu Guilong1,Zhang Yinggan7,Han Jiajia7,Liu Xianming1,Chen Xiang8,Wan Jiayu9,Zhang Qiaobao710

Affiliation:

1. College of Chemistry and Chemical Engineering and Luoyang Key Laboratory of Green Energy Materials Luoyang Normal University Luoyang 471934 China

2. Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Carbon Neutrality University of Science and Technology Beijing Beijing 100083 China

3. Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang 110004 China

4. Shanxi Key Laboratory of Advanced Magnesium‐based Materials College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 China

5. New Energy Technology Engineering Lab of Jiangsu Province College of Science Nanjing University of Posts & Telecommunications (NUPT) Nanjing 210023 China

6. Henan Key Laboratory of High‐temperature Structural and Functional Materials National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials Henan University of Science and Technology Luoyang 471003 China

7. State Key Laboratory of Physical Chemistry of Solid Surfaces College of Materials Xiamen University Xiamen 361005 China

8. Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing 100084 China

9. Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Shanghai 200240 China

10. Shenzhen Research Institute of Xiamen University Shenzhen 518000 China

Abstract

AbstractMachine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, the discovery and development of advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to the background of SSEs, including their explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing their conductivity, challenges, and future developments. An in‐depth explanation of the ML methodology is also elucidated. Subsequently, the key factors that influence the performance of SSEs are summarized, including thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, and activation energy. Finally, it is offered perspectives on the design prerequisites for upcoming generations of SSEs, focusing on real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation and high‐throughput experimentation, and synergistic optimization of full battery, all of which are crucial for accelerating the progress in SSEs. This review aims to guide the design and optimization of novel SSE materials for the practical realization of efficient and reliable SSEs in energy storage technologies.

Funder

National Natural Science Foundation of China

Publisher

Wiley

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