PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF

Wheeler, William; Pathak, Shivesh; Kleiner, Kevin G.; Yuan, Shunyue; Rodrigues, J. N. B.; Cooper, Lorsung,; Krongchon, Kittithat; Chang, Yueqing; Zhou, Yiqing; Busemeyer, Brian; Williams, Kiel T.; Alexander, Muñoz,; Chow, Chun Yu; Wagner, Lucas K.

doi:10.48550/arxiv.2212.01482

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“…This work is developed upon open-source FermiNet 47 and PyQMC 48 on Github, deep learning framework JAX 49 is used which supports flexible and powerful complex number calculation. Ground-state energy calculations are performed with all-electrons.…”

Section: Workflow and Computational Detailsmentioning

confidence: 99%

Ab initio calculation of real solids via neural network ansatz

Li¹,

Li²,

Chen

2022

Nat Commun

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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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Section: Workflow and Computational Detailsmentioning

confidence: 99%