Explore auxiliary-field quantum Monte Carlo (AFQMC) methods for quantum materials in this 49-minute lecture presented by Shiwei Zhang from the Flatiron Institute's Center for Computational Quantum Physics. Gain insights into the connections between AFQMC and other quantum Monte Carlo methods, as well as its relation to neural networks. Discover recent algorithmic advances in ab initio simulations of solids, including correlated sampling, computation of gradients (forces and stresses), and structural optimization. Delve into topics such as the many-electron problem, challenges in accurate calculations, and strategies to understand and control the sign problem. Learn about applications in transition metal complexes, electrochemistry, and lattice optimization using AI. Understand the computation of observables, correlations, and the implementation of algorithms for noisy forces and stresses in quantum materials research.