ArticleNature communications2022
Ab initio calculation of real solids via neural network ansatz.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Accurate Atomic Correlation and Total Energies for Correlation-Consistent Effective Core Potentials II: Rb-Xe Elements.Journal of chemical theory and computation · 2026Article
- Transferable neural wavefunctions for solids.Nature computational science · 2025Article
- A multi-resolution systematically improvable quantum embedding scheme for large-scale surface chemistry calculations.Nature communications · 2025Article
- Towards a transferable fermionic neural wavefunction for molecules.Nature communications · 2024Article
- Ab initio quantum chemistry with neural-network wavefunctions.Nature reviews. Chemistry · 2023Review
- Towards the ground state of molecules via diffusion Monte Carlo on neural networks.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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