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<p>Neural network-based quantum Monte Carlo (NNQMC), an emerging method for solving many-body quantum systems with high accuracy, has been mainly applied to small systems owing to demanding computation requirements. Here we introduce a framework based on local pseudopotentials to break through such limitation, improving the computational efficiency and scalability of NNQMC. The incorporation of local pseudopotentials reduces the number of electrons treated in neural network and also achieves better relative energy accuracy than all electron NNQMC calculations for complex systems. This counterintuitive outcome is made possible by the distinctive characteristics inherent to NNQMC. Notably, by avoiding costly integration terms, this approach is also substantially more efficient than its widely used semilocal counterparts. Our approach enables the reliable treatment of large and challenging systems, such as the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\text{Fe}}_{4}{\text{S}}_{4}{({\text{SCH}}_{3})}_{4}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mrow> <mi mathvariant="normal">Fe</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msub> <msub> <mrow> <mi mathvariant="normal">S</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msub> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mrow> <mi mathvariant="normal">SCH</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mn>4</mn> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> iron–sulfur cluster. Overall, our findings demonstrate that the synergy between NNQMC and local pseudopotentials substantially expands the scope of accurate ab initio calculations.</p>

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Empowering neural network-based quantum Monte Carlo with local pseudopotentials

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Weizhong Fu,
  • Ryunosuke Fujimaru,
  • Ruichen Li,
  • Yuzhi Liu,
  • Xuelan Wen,
  • Xiang Li,
  • Kenta Hongo,
  • Liwei Wang,
  • Tom Ichibha,
  • Ryo Maezono,
  • Ji Chen,
  • Weiluo Ren

摘要

Neural network-based quantum Monte Carlo (NNQMC), an emerging method for solving many-body quantum systems with high accuracy, has been mainly applied to small systems owing to demanding computation requirements. Here we introduce a framework based on local pseudopotentials to break through such limitation, improving the computational efficiency and scalability of NNQMC. The incorporation of local pseudopotentials reduces the number of electrons treated in neural network and also achieves better relative energy accuracy than all electron NNQMC calculations for complex systems. This counterintuitive outcome is made possible by the distinctive characteristics inherent to NNQMC. Notably, by avoiding costly integration terms, this approach is also substantially more efficient than its widely used semilocal counterparts. Our approach enables the reliable treatment of large and challenging systems, such as the \({\text{Fe}}_{4}{\text{S}}_{4}{({\text{SCH}}_{3})}_{4}\) Fe 4 S 4 ( SCH 3 ) 4 iron–sulfur cluster. Overall, our findings demonstrate that the synergy between NNQMC and local pseudopotentials substantially expands the scope of accurate ab initio calculations.