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Explores atomic descriptors, emphasizing symmetry, locality, and the challenges of incorporating electrostatics in machine learning models for chemistry.
Covers classical electrodynamics, emphasizing the importance of attending live sessions, the mathematical nature of the course, and upcoming topics on Maxwell equations and special relativity.
Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
