Predicting Energies from Electron Densities - Machine Learning for Reactive Molecular Dynamics

Predicting Energies from Electron Densities - Machine Learning for Reactive Molecular Dynamics

Explore the application of machine learning in predicting energies from electron densities for reactive molecular dynamics in this 28-minute talk by NYU's Leslie Vogt-Maranto. Delve into topics such as generating free energy surfaces, calculating observables, and machine learning molecular energies. Examine the use of Density Functional Theory (DFT) in energy calculations via electron densities, and learn about machine learning approaches for DFT in molecules, hydrogen, and various compounds. Discover sampling strategies for training geometries and investigate the overlap of test and training data. Gain insights into future directions and ongoing developments in this cutting-edge field of computational chemistry and physics.