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Ab initio calculation of real solids via neural network ansatz

Author

Listed:
  • Xiang Li

    (ByteDance Inc)

  • Zhe Li

    (ByteDance Inc)

  • Ji Chen

    (Peking University)

Abstract

Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose an architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solids. The accuracy of our approach is demonstrated in four different types of systems, namely the one-dimensional periodic hydrogen chain, the two-dimensional graphene, the three-dimensional lithium hydride crystal, and the homogeneous electron gas, where the obtained results, e.g. total energies, dissociation curves, and cohesive energies, reach a competitive level with many traditional ab initio methods. Moreover, electron densities of typical systems are also calculated to provide physical intuition of various solids. Our method of extending a molecular neural network to periodic systems can be easily integrated into other neural network structures, highlighting a promising future of ab initio solution of more complex solid systems using neural network ansatz, and more generally endorsing the application of machine learning in materials simulation and condensed matter physics.

Suggested Citation

  • Xiang Li & Zhe Li & Ji Chen, 2022. "Ab initio calculation of real solids via neural network ansatz," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35627-1
    DOI: 10.1038/s41467-022-35627-1
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    References listed on IDEAS

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    1. Sugiyama, G. & Zerah, G. & Alder, B.J., 1989. "Ground-state properties of metallic lithium," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 156(1), pages 144-168.
    2. Kenny Choo & Antonio Mezzacapo & Giuseppe Carleo, 2020. "Fermionic neural-network states for ab-initio electronic structure," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    3. George H. Booth & Andreas Grüneis & Georg Kresse & Ali Alavi, 2013. "Towards an exact description of electronic wavefunctions in real solids," Nature, Nature, vol. 493(7432), pages 365-370, January.
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    Cited by:

    1. Michael Scherbela & Leon Gerard & Philipp Grohs, 2024. "Towards a transferable fermionic neural wavefunction for molecules," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Weiluo Ren & Weizhong Fu & Xiaojie Wu & Ji Chen, 2023. "Towards the ground state of molecules via diffusion Monte Carlo on neural networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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