Ab initio calculation of real solids via neural network ansatz

Xiang Li, Zhe Li, Ji Chen

Neural networks have been applied to tackle many-body electron correlations
for small molecules and physical models in recent years. Here we propose a new
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, outperform many traditional ab
initio methods and reach the level of the most accurate approaches. 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.