深度势能方法及其在电化学储能材料中的应用

doi:10.19799/j.cnki.2095-4239.2024.0699

摘要/Abstract

摘要：

深度势能模型(deep potential，DP)通过先进的机器学习技术，从大量的原子结构和能量数据中提取知识，构建出高精度的势能面。这一创新方法有效突破了传统力场方法的局限，为材料科学领域带来了新的视角。本文概述了DP模型和软件的基本原理、开发进展与应用流程，回顾了其在电化学储能材料设计中的应用，展示了DP模型在揭示电池材料微观结构和动力学行为方面的优势。在正负极材料的研究中，精确描述脱嵌锂过程中材料的结构演变和自由能变化；在固态电解质的研究中，精确描述了材料结构与离子输运行为；在电解液的研究中，不仅提高了对溶液动态结构和性质的认识，还为氧化还原电位、酸度等物理化学性质的精确计算提供了新策略；在界面的研究中，准确解析了界面形成过程中的结构演变以及性质。这些对材料的准确描述均有利于加速对能源材料的开发。同时，指出了DP模型在电池材料模拟中仍需改进的问题，并展望了其在电池材料设计和优化中的潜在应用前景。结果说明，深度势能模型作为一种强大的计算工具，在电化学储能材料的研究中展现出巨大的应用潜力。通过不断的模型优化和算法创新，DP模型有望在未来的材料设计和电池技术发展中发挥更加关键的作用。

关键词: 深度势能, 分子模拟, 储能材料, 神经网络

Abstract:

The deep potential (DP) model constructs high-precision potential energy surfaces by leveraging advanced machine learning techniques to extract knowledge from vast amounts of atomic structure and energy data. This innovative approach overcomes the limitations of traditional force field methods and offers new insights into materials science. This study outlines the basic principles, development process, and application flow of the proposed DP model and its software. This study reviews the application of the DP model in electrochemical energy storage materials and highlights its advantages in revealing the microstructure and kinetic behavior of battery materials. The model accurately describes the structural evolution and free energy changes during lithium deintercalation in the cathode and anode materials. The material structure and ion transport behavior of solid electrolytes are precisely captured for solid electrolytes. For electrolytes, the model not only enhances the understanding of their dynamic structures and properties but also offers a new strategy for accurately calculating their physicochemical properties, such as their redox potential and acidity. For interfaces, the model resolves the structural evolution and properties during interface formation. These accurate material descriptions facilitate the accelerated development of energy materials. In addition, the study identifies areas for improvement in simulating battery materials using the DP model and envisions its potential applications in battery material design and optimization. The results demonstrate that the proposed DP model, as a powerful computational tool, has great potential for studying electrochemical energy storage materials. With ongoing model optimization and algorithmic innovation, the DP model is expected to play an increasingly vital role in future material design and battery technology development.

Key words: deep potential, molecular simulation, energy storage materials, neural networks

中图分类号:

TM 91

邓斌, 华海明, 张与之, 王晓旭, 张林峰. 深度势能方法及其在电化学储能材料中的应用[J]. 储能科学与技术, 2024, 13(9): 2884-2906.

Bin DENG, Haiming HUA, Yuzhi ZHANG, Xiaoxu WANG, Linfeng ZHANG. Deep potential model: Applications and insights for electrochemical energy storage materials[J]. Energy Storage Science and Technology, 2024, 13(9): 2884-2906.

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