Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design

Abstract Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural network potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the…

• NN
  National Natural Science Foundation of China

  Award: 12374008
• NK
  National Key Research and Development Program of China

  Award: 2023YFB3003001
• FR
  Fundamental Research Funds for the Central Universities
• PF
  Program for Jilin University Science and Technology Innovative Research Team