Deep potential model:Applications and insights for electrochemical energy storage materials
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.
deep potentialmolecular simulationenergy storage materialsneural networks
万方数据
维普
