Explore the application of neural-network wave functions in quantum chemistry through this 51-minute lecture presented by Jan Hermann from Freie Universität Berlin at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop. Delve into variational quantum Monte Carlo techniques applied to arbitrary Hilbert spaces and Hamiltonians, and discover how neural network-based wave-function ansatzes can be integrated into first- and second-quantization formalisms. Examine two key applications: solving ab-initio electronic Hamiltonians for molecules using an antisymmetric ansatz, and addressing a model exciton-phonon Hamiltonian with a convolutional neural network. Learn about ground and excited state calculations, energy barrier computations, and the advantages of these approaches over established quantum chemistry methods. Gain insights into quantum mechanics for electrons, discrete basis states, second quantization, and the practical implementation of these techniques in quantum chemistry research. Read more