End-to-End Learning and Auto-Differentiation - Forces, Uncertainties, Etc.

Explore cutting-edge applications of deep learning and auto-differentiation in molecular simulations through this 56-minute lecture by Rafael Gomez-Bombarelli from MIT. Delve into topics such as active learning of machine learning potentials, deep neural network generative models for coarse-grained atomic system representations, and differentiable simulations for reaction path finding. Examine the challenges of overfitting and generalizability in AI for science, discussing the scalability of active learning and sensitivity of deep learning solutions in molecular problems. Gain insights into excited state potentials, uncertainty in interatomic potentials, coarse-graining techniques, and the use of equivariant generative decoders in molecular liquid simulations.