  Establishing a predictive ab initio method for solid systems is one of the
fundamental goals in condensed matter physics and computational materials
science. The central challenge is how to encode a highly-complex
quantum-many-body wave function compactly. Here, we demonstrate that artificial
neural networks, known for their overwhelming expressibility in the context of
machine learning, are excellent tool for first-principles calculations of
extended periodic materials. We show that the ground-state energies in real
solids in one-, two-, and three-dimensional systems are simulated precisely,
reaching their chemical accuracy. The highlight of our work is that the
quasiparticle band spectra, which are both essential and peculiar to
solid-state systems, can be efficiently extracted with a computational
technique designed to exploit the low-lying energy structure from neural
networks. This work opens up a path to elucidate the intriguing and complex
many-body phenomena in solid-state systems.
