Molecular Electron Densities via Machine Learning - IPAM at UCLA

Explore a conference talk on using machine learning to predict molecular electron densities and energies for quantum mechanics applications. Delve into Leslie Vogt-Maranto's presentation at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop, covering topics such as Kohn-Sham density functional theory, mapping nuclear potential to electron density, and leveraging delta-learning for ab initio energy calculations. Discover how these machine learning models can generate molecular dynamics trajectories, sample strained geometries, and capture conformer changes with sufficient accuracy. Gain insights into the potential of combining machine learning with quantum mechanics for more efficient and accurate molecular simulations.