Deep Neural Network Solution of the Electronic Schrödinger Equation

This lecture covers the application of deep neural networks to solve the electronic Schrödinger equation, focusing on variational approaches, Slater determinants, correlation effects, trainable functions, and ground state energies of various systems. It discusses the challenges of incorporating antisymmetry, cusp conditions, and strong correlation effects in the wave function ansatz. The training methodology, system studies including lithium hydride and hydrogen chains, and the accuracy comparison with traditional methods are also presented. The lecture concludes by highlighting the computational efficiency and complementarity of neural network approaches with established many-body physics theories.