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Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores classical and quantum mechanics, covering observables, momentum, Hamiltonian, and the Schrödinger equation, as well as quantum chemistry and the Schrödinger's cat experiment.
Covers classical force fields, molecular dynamics simulations, and supramolecular properties, including intramolecular and intermolecular interactions.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
