Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network

The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin–gap calculations, including the singlet–triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems. An efficient approach is developed to enforce spin symmetry for neural network wavefunctions when solving the many-body Schrödinger equation. This enables accurate and spin-pure simulations of both ground and excited states.