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@njzjz Here is the BibTeX entry for 2312.15492 : bibtex @Article{Zhang_arXiv_2023_p2312.15492, author = {Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and Chun Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Jiameng Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang and Siyuan Liu and Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang and Jing Wu and Yudi Yang and Jiyuan Yang and Manyi Yang and Fu-Qiang Gong and Linshuang Zhang and Mengchao Shi and Fu-Zhi Dai and Darrin M. York and Shi Liu and Tong Zhu and Zhicheng Zhong and Jian Lv and Jun Cheng and Weile Jia and Mohan Chen and Guolin Ke and Weinan E and Linfeng Zhang and Han Wang}, title = {{DPA-2: Towards a universal large atomic model for molecular and material simulation}}, journal = {arXiv}, year = 2023, pages = {2312.15492}, doi = {10.48550/arXiv.2312.15492}, annote = {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.}, }

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[github-actions]

@njzjz Here is the BibTeX entry for 2312.15492 :

@Article{Zhang_arXiv_2023_p2312.15492,
    author =   {Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and
             Chun Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Jiameng
             Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang and
             Siyuan Liu and Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou
             and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang
             and Jing Wu and Yudi Yang and Jiyuan Yang and Manyi Yang and Fu-Qiang
             Gong and Linshuang Zhang and Mengchao Shi and Fu-Zhi Dai and Darrin M.
             York and Shi Liu and Tong Zhu and Zhicheng Zhong and Jian Lv and Jun
             Cheng and Weile Jia and Mohan Chen and Guolin Ke and Weinan E and
             Linfeng Zhang and Han Wang},
    title =    {{DPA-2: Towards a universal large atomic model for molecular and
             material simulation}},
    journal =  {arXiv},
    year =     2023,
    pages =    {2312.15492},
    doi =      {10.48550/arXiv.2312.15492},
    annote =   {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.},
}