High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks

Establishing quantum spin liquid physics in microscopic models is a daunting task due to a variety of competing low-energy states, requiring sophisticated computational approaches to find the true ground state. Here, the authors present such a technique, based on neural networks with space-group symmetry, to obtain significantly improved ground-state wave functions of frustrated Heisenberg models. The authors also use this approach to find excited states and provide a blueprint for testing analytical predictions from spin-liquid theories.