Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen

Explore advanced quantum simulation techniques in this 50-minute conference talk by David Ceperley from the University of Illinois at Urbana-Champaign. Delve into the development of Coupled-Electron Quantum Monte Carlo (QMC) methods for simulating dense hydrogen, incorporating sophisticated wavefunctions and utilizing reptation QMC for electronic energies alongside Path Integral MC for proton distribution. Discover recent advancements in calculating electronic energy gaps, enabling direct comparisons with experimental measurements. Examine the creation of a database of forces on protons in dense hydrogen configurations using QMC, and learn how machine-learned force fields trained on this data predict novel solid hydrogen structures. Gain insights into cutting-edge applications of Monte Carlo and machine learning approaches in quantum mechanics, presented at IPAM's workshop on the subject.

David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen