Application Progress of Machine Learning Interatomic Potential Molecular Dynamics Simulations in the Research of Electrochemical Energy Storage Materials(PDF)
MATERIALS CHINA[ISSN:1674-3962/CN:61-1473/TG]
Issue:
2025年04
Page:
330-348
Research Field:
Publishing date:
Info
Title:
Application Progress of Machine Learning Interatomic Potential Molecular Dynamics Simulations in the Research of Electrochemical Energy Storage Materials
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
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.