  The rapid development of artificial intelligence (AI) is driving significant
changes in the field of atomic modeling, simulation, and design. AI-based
potential energy models have been successfully used to perform large-scale and
long-time simulations with the accuracy of ab initio electronic structure
methods. However, the model generation process still hinders applications at
scale. We envision that the next stage would be a model-centric ecosystem, in
which a large atomic model (LAM), pre-trained with as many atomic datasets as
possible and can be efficiently fine-tuned and distilled to downstream tasks,
would serve the new infrastructure of the field of molecular modeling. We
propose DPA-2, a novel architecture for a LAM, and develop a comprehensive
pipeline for model fine-tuning, distillation, and application, associated with
automatic workflows. We show that DPA-2 can accurately represent a diverse
range of chemical systems and materials, enabling high-quality simulations and
predictions with significantly reduced efforts compared to traditional methods.
Our approach paves the way for a universal large atomic model that can be
widely applied in molecular and material simulation research, opening new
opportunities for scientific discoveries and industrial applications.
