Universal materials model of deep-learning density functional theory Hamiltonian

Authors:
  • YWYuxiang Wang
    Yuxiang Wang
  • YLYang Li
    Yang Li
  • ZTZechen Tang
    Zechen Tang
  • HLHe Li
    He Li
  • ZYZilong Yuan
    Zilong Yuan
  • HTHonggeng Tao
    Honggeng Tao
  • NZNianlong Zou
    Nianlong Zou
  • TBTing Bao
    Ting Bao
  • XLXinghao Liang
    Xinghao Liang
  • ZCZezhou Chen
    Zezhou Chen
  • SXShanghua Xu
    Shanghua Xu
  • CBCe Bian
    Ce Bian
Preprint
Publisher website
Google Scholar
Abstract
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.