文章信息/Info

Title:

Application Progress of Machine Learning Interatomic Potential Molecular Dynamics Simulations in the Research of Electrochemical Energy Storage Materials

文章編號:

1674-3962(2025)04-0330-19

作者:

林奕希; 蔣雨橋; 馮相民; 要騰宇; 夏穎慧; 劉振輝; 鄭明波; 申來法; 許真銘

南京航空航天大學(xué)材料科學(xué)與技術(shù)學(xué)院 江蘇省高效電化學(xué)儲能技術(shù)重點實驗室，江蘇 南京 210016

Author(s):

LIN Yixi; JIANG Yuqiao; FENG Xiangmin; YAO Tengyu; XIA Yinghui; LIU Zhenhui; ZHENG Mingbo; SHEN Laifa; XU Zhenming

Jiangsu Key Laboratory of Materials and Technologies for Energy Storage, College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

關(guān)鍵詞:

分子動力學(xué)模擬; 第一性原理計算; 機器學(xué)習(xí); 分子力場; 電化學(xué)儲能材料

Keywords:

molecular dynamics simulation;  first�瞤rinciples calculation;  machine learning;  molecular force field;  electrochemical energy storage materials

分類號:

TP181; TM912； TB34

DOI:

10.7502/j.issn.1674-3962.202409017

文獻標(biāo)志碼:

A

摘要:

電化學(xué)儲能材料研究領(lǐng)域?qū)Ψ肿幽�擬有著切實的需求，而經(jīng)典分子動力學(xué)和從頭算分子動力學(xué)模擬因無法兼顧精度和效率的問題限制了分子模擬的廣泛應(yīng)用。近年來，基于機器學(xué)習(xí)方法構(gòu)建原子間勢模型得到了快速的發(fā)展，機器學(xué)習(xí)原子間勢分子動力學(xué)模擬可以兼顧經(jīng)典分子動力學(xué)模擬的計算效率和從頭算分子動力學(xué)模擬的準(zhǔn)確性。為了更好地呈現(xiàn)機器學(xué)習(xí)原子間勢分子動力學(xué)模擬技術(shù)在電化學(xué)儲能材料研究領(lǐng)域的應(yīng)用進展和前景，重點介紹了其在固體電解質(zhì)、電解液、電極/電解質(zhì)（液）界面等研究領(lǐng)域的應(yīng)用，并總結(jié)了材料領(lǐng)域機器學(xué)習(xí)原子間勢及其分子動力學(xué)模擬所存在的挑戰(zhàn)和機遇。

Abstract:

There is a growing demand for molecular simulations in the field of electrochemical energy storage materials research. However, the widespread application of molecular simulations has been limited by the inability of the classical molecular dynamics and ab-initio molecular dynamics to balance the accuracy and efficiency. In recent years, the machine learning-based models for interatomic potentials have developed rapidly, offering the potential for the machine learning interatomic potential molecular dynamics (MLMD) simulations to achieve both the computational efficiency of the classical molecular dynamics and the accuracy of the ab-initio molecular dynamics. To better present the advancements and prospects of the MLMD simulation technology in the research of the electrochemical energy storage materials, this work focuses on its applications in solid electrolytes, electrolytes, and electrode/electrolyte interfaces, and summarizes the challenges and opportunities for the machine learning interatomic potentials and their molecular dynamics simulations in the materials field.