This study introduces a novel artificial intelligence(AI)force field,namely a graph-based pre-trained transformer force field(GPTFF),which can simulate arbitrary inorganic systems with good precision and generalizabil...This study introduces a novel artificial intelligence(AI)force field,namely a graph-based pre-trained transformer force field(GPTFF),which can simulate arbitrary inorganic systems with good precision and generalizability.Harnessing a large trove of the data and the attention mechanism of transformer algorithms,the model can accurately predict energy,atomic force,and stress with mean absolute error(MAE)values of 32 me V/atom,71 me V/A,and 0.365 GPa,respectively.The dataset used to train the model includes 37.8 million single-point energies,11.7 billion force pairs,and 340.2 million stresses.We also demonstrated that the GPTFF can be universally used to simulate various physical systems,such as crystal structure optimization,phase transition simulations,and mass transport.The model is publicly released with this paper,enabling anyone to use it immediately without needing to train it.展开更多
基金supported by the National Natural Science Foundation of China(12025407 and 11934003)Chinese Academy of Sciences(CAS-WX2023SF-0101,XDB33020000,XDB33030100)National Key R&D Program of China(2021YFA0718700,2021YFA1400200)。
文摘This study introduces a novel artificial intelligence(AI)force field,namely a graph-based pre-trained transformer force field(GPTFF),which can simulate arbitrary inorganic systems with good precision and generalizability.Harnessing a large trove of the data and the attention mechanism of transformer algorithms,the model can accurately predict energy,atomic force,and stress with mean absolute error(MAE)values of 32 me V/atom,71 me V/A,and 0.365 GPa,respectively.The dataset used to train the model includes 37.8 million single-point energies,11.7 billion force pairs,and 340.2 million stresses.We also demonstrated that the GPTFF can be universally used to simulate various physical systems,such as crystal structure optimization,phase transition simulations,and mass transport.The model is publicly released with this paper,enabling anyone to use it immediately without needing to train it.
