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<p>First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy–efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery.</p>

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Deep-learning electronic structure calculations

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Zechen Tang,
  • Haoxiang Chen,
  • Yang Li,
  • Yubing Qian,
  • Yuxiang Wang,
  • Weizhong Fu,
  • Jialin Li,
  • Chen Si,
  • Wenhui Duan,
  • Ji Chen,
  • Yong Xu

摘要

First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy–efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery.