Extending the Reach of Quantum Monte Carlo Methods via Machine Learning

Extending the Reach of Quantum Monte Carlo Methods via Machine Learning

Explore a conference talk on extending quantum Monte Carlo methods through machine learning. Discover how active learning with AMPTorch predicts QMC-quality forces for molecular geometry relaxation and dynamics simulations. Learn about using Gaussian Process Regression to accurately predict solid energies in the thermodynamic limit. Examine case studies on carbon dimers, water, and H2O molecules, and understand the challenges of short bond lengths. Investigate the effects of statistical error bars and the generalization to multiple degrees of freedom. Gain insights into coupling machine learning methods with quantum Monte Carlo techniques to enable and accelerate complex calculations.