Work

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

Year: 2024

Type: article

Abstract: 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 meV/atom, 71 meV/Å, 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.

Source: 中国科学通报：英文版

DOI: 10.1016/j.scib.2024.08.039

Authors: Fankai Xie, Tenglong Lu, Sheng Meng, Miao Liu

Institutions: Chinese Academy of Sciences, Songshan Lake Materials Laboratory, Institute of Physics

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,880