生成式人工智能在电池研究中的应用：现状与展望

引用本文: 张恒睿, 徐熹骏, 李璕琭, 杲祥文. 生成式人工智能在电池研究中的应用：现状与展望[J]. 物理化学学报, 2025, 41(10): 100115. doi: 10.1016/j.actphy.2025.100115 shu

Citation: Hengrui Zhang, Xijun Xu, Xun-Lu Li, Xiangwen Gao. Applications of Generative Artificial Intelligence in Battery Research: Current Status and Prospects[J]. Acta Physico-Chimica Sinica, 2025, 41(10): 100115. doi: 10.1016/j.actphy.2025.100115 shu

生成式人工智能在电池研究中的应用：现状与展望

上海交通大学溥渊未来技术学院, 未来电池研究中心, 上海 200240

通讯作者: 李璕琭, xunlu.li@sjtu.edu.cn; 杲祥文, xiangwen.gao@sjtu.edu.cn

基金项目:
上海交通大学“新进青年教师启动计划” 23X010502145

摘要: 随着可再生能源与电动交通工具的快速发展，电池作为电化学储能系统的核心组件已成为全球科研与产业界的关注焦点。然而，电池内部复杂的多物理场耦合效应使得传统数学模型难以全面揭示其反应及失效机理，而数据驱动方法通过大量积累实验数据并从中提取有效信息，为电池研发奠定了坚实的基础。生成式人工智能(GAI)凭借其强大的潜在规律学习与数据生成能力，在蛋白质结构预测、材料逆向设计、数据增强等方面得到了广泛的应用，在电池多尺度研究中具有广阔的应用前景。本文阐释了生成式模型(GM)的核心原理，并从材料设计、微观表征及状态估计三方面综述了其在电池研究中的最新进展，最后探讨了其当前面临的挑战及未来发展方向，为将GAI这一创新解决方案运用于电池研发的工作流中提供了理论参考和实践依据。

关键词:

English

Applications of Generative Artificial Intelligence in Battery Research: Current Status and Prospects

Future Battery Research Center, Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding author: Email: xunlu.li@sjtu.edu.cn (X.-L. Li); Email: xiangwen.gao@sjtu.edu.cn (X. Gao)

Received Date: 03 April 2025
Accepted Date: 10 June 2025
Revised Date: 24 May 2025
Available Online: 15 October 2025
Fund Project:
the Startup Fund for Young Faculty at SJTU

Abstract: With the rapid development of renewable energy and electric vehicles, batteries, as the core components of electrochemical energy storage systems, have become a global focus in both scientific research and industrial sectors due to their critical impact on system efficiency and safety. However, the complex multi-physics reactions within batteries make traditional mathematical models inadequate for comprehensively revealing their mechanisms. The key to solving this problem lies in introducing data-driven approaches, which have laid a solid foundation for battery research and development through extensive accumulation of experimental data and extraction of effective information. Generative artificial intelligence (GAI), leveraging its powerful latent pattern learning and data generation capabilities, has already found widespread applications in protein structure prediction, material inverse design, and data augmentation, demonstrating its broad application prospects. Applying GAI to battery research workflows with diverse battery data resources could provide innovative solutions to challenges in battery research. In this perspective, we introduce the core principles and latest advancements of generative models (GMs), including Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), and Diffusion Model (DM), which can learn the latent distribution of the input samples to generate new data by sampling from it. Applications of GAI in battery research are then reviewed. For battery materials design, by learning material compositions, structures, and properties, GM can generate novel candidate materials with desired properties through conditional constraints, significantly extending the chemical space to be explored. For electrode microstructure characterization, GM can serve as a bridge for interconversion and integration of different image data, enhance the quality of microscopic characterization, and generate realistic synthetic data. For battery state estimation, GM can perform data augmentation and feature extraction on battery datasets, which benefits the model performance for battery state estimation. Lastly, we discuss the challenges and future development directions in terms of data governance and model design, including data quality and diversity, data standardization and sharing, usability of synthetic data, interpretability of GM, and foundational models for battery research. For the innovation and advancement of battery technology, this perspective offers theoretical references and practical guidelines for implementing GAI as an effective tool in battery research workflows by discussing its status and prospects in this field.

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沈阳化工大学材料科学与工程学院沈阳 110142

生成式人工智能在电池研究中的应用：现状与展望

通讯作者: 李璕琭, xunlu.li@sjtu.edu.cn; 杲祥文, xiangwen.gao@sjtu.edu.cn

English

Applications of Generative Artificial Intelligence in Battery Research: Current Status and Prospects

Corresponding author: Email: xunlu.li@sjtu.edu.cn (X.-L. Li); Email: xiangwen.gao@sjtu.edu.cn (X. Gao)

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

生成式人工智能在电池研究中的应用：现状与展望

通讯作者: 李璕琭, xunlu.li@sjtu.edu.cn; 杲祥文, xiangwen.gao@sjtu.edu.cn

English

Applications of Generative Artificial Intelligence in Battery Research: Current Status and Prospects

Corresponding author: Email: xunlu.li@sjtu.edu.cn (X.-L. Li); Email: xiangwen.gao@sjtu.edu.cn (X. Gao)

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

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