Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models.…

• NN
  National Natural Science Foundation of China

  Awards: 52332005, 2022TQ0019
• CP
  China Postdoctoral Science Foundation

  Awards: 52332005, 2022TQ0019
• NK
  National Key Research and Development Program of China

  Award: 2022YFB3807200