GPTFF:A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials  

GPTFF:一套高精度开箱即用的无机化合物人工智能通用力场模型

作　　者：Fankai Xie Tenglong Lu Sheng Meng Miao Liu 谢帆恺;芦腾龙;孟胜;刘淼

机构地区：[1]Beijing National Laboratory for Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China [2]Songshan Lake Materials Laboratory,Dongguan 523808,China

出　　处：《Science Bulletin》2024年第22期3525-3532,共8页科学通报（英文版）

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)。

摘　　要：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.

关 键 词：Data science Molecular dynamics Graph neural network Universal force field 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O611[自动化与计算机技术—控制科学与工程]