Foundations
Mathematical, physical, chemical, and biological ideas that scientific machine learning builds on.
- A physics-first account of molecular representation, electronic structure, forces, statistical mechanics, and dynamics for molecular machine learning.
- A concrete account of group actions, invariance, equivariance, and feature types for geometric machine learning.