Extending Molecular Neural Networks to Periodic Systems

Ab initio calculation of real solids via neural network ansatz

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 abinitio methods and reach the level of the most accurate approaches.