摘要
The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and design.AI-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure methods.However,the model generation process remains a bottleneck for large-scale applications.We propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular modeling.In this study,we introduce the DPA-2 architecture as a prototype for LAMs.Pre-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies.Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.
基金
supported by the National Key R&D Program of China(grantno.2022YFA1004300)
the National Natural Science Foundation of China(grant no.12122103)
supported by the National Key Research and Development Project of China(grant no.2022YFA1004302)
the National Natural Science Foundation of China(grants nos.92270001 and 12288101)
supported by the National Institutes of Health(grant no.GM107485 to D.M.Y.)
the National Science Foundation(grant no.2209718 to D.M.Y.)
supported by the Natural Science Foundation of Zhejiang Province(grant no.2022XHSJJ006)
supported by the National Natural Science Foundation of China(grants nos.22222303 and 22173032)
supported by the National Key R&D Program of China(grants nos.2021YFA0718900 and 2022YFA1403000)
supported by the National Natural Science Foundation of China(grants nos.12034009 and 91961204)
supported by the National Science Fund for Distinguished Young Scholars(grant no.22225302)
Laboratory of AI for Electrochemistry(AI4EC),and IKKEM(grants nos.RD2023100101 and RD2022070501)
supported by the National Natural Science Foundation of China(grants nos.12122401,12074007,and 12135002)
Lastly,the computational resource was supported by the Bohrium Cloud Platform at DP Technology and Tan Kah Kee Supercomputing Center(IKKEM).
