基于大数据的电池新材料设计

Discovery of new battery materials based on a big data approach

固态电池是极具潜力的下一代储能器件之一,寻找综合性能优异的电池材料有望从根本上提升电池的性能。本文围绕固态电池中离子传输、表面/界面现象以及微观结构动态演化等关键科学问题,介绍了基于多精度传递思想的高通量材料筛选策略,以及机器学习技术在加速模拟复杂物理化学过程、解析电池内部复杂构效关系的突出作用。受益于多精度传递思想和机器学习技术的应用,可以从直接筛选、元素替换、结构单元搭建、非晶结构构建等多个角度高效获得快离子导体材料,多维度解析离子传输性能与微观机制,极大丰富了电极材料、固态电解质材料等候选范围。此外,针对电池材料研发流程的云工具箱提供了数据归档、分析及复用等多项功能,旨在使得借助大数据和人工智能的材料研发流程更为自动化。本文介绍的基于大数据的研究思路和研究模式,结合新兴的机器学习技术,能够有效加速新型电池材料的设计和开发进程,深化对电池内部复杂物理化学现象的理解,为设计实用的新型固态电池材料赋能。

Solid-state batteries are one of the most promising next-generation energy storage technologies.Designing new battery materials with excellent comprehensive performance is expected to improve the performance of batteries.This article focuses on key scientific issues in solid-state batteries,such as migrated ion transport behavior,complex surface/interface phenomena,and microstructural dynamic evolution.Furthermore,it proposes a high-throughput material screening strategy based on multiprecision transmission.It also discusses the outstanding role of machine learning techniques in accelerating the simulation of complex physical and chemical processes and analyzing the structure-properties relationships of batteries.Benefiting from the application of multiprecision transmission concepts and machine learning techniques,the efficient acquisition of fast ion conductor materials can be achieved from various perspectives,such as direct screening,element substitution,crystal structure construction,and the design of amorphous structures.This allows for a multidimensional analysis of ionic transport performance and microscopic mechanisms,greatly enriching the range of candidate materials for electrode and solid-state electrolyte materials.In addition,the cloud toolkit designed for the development process of battery materials provides multiple features,such as data archiving,analysis,and reutilization,aimed at automating the material development process.The research paradigm and models based on big data introduced in this article,together with emerging machine learning technologies,can effectively accelerate the design and development process of new battery materials,deepen the understanding of complex physical and chemical phenomena inside batteries,and promote the design of practical new solid-state battery materials.