  This study introduces a novel AI force field, namely 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 forces, and stress with Mean Absolute Error (MAE) values
of 32 meV/atom, 71 meV/{\AA}, 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 GPTFF can be
universally used to simulate various physical systems, such as crystal
structure optimization, phase transition simulations, and mass transport.
