Explores classical and quantum mechanics, covering observables, momentum, Hamiltonian, and the Schrödinger equation, as well as quantum chemistry and the Schrödinger's cat experiment.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Summarizes Generalized Gradient Approximations, Meta-GGAs, Hybrid functionals, First-Principles Molecular Dynamics, QM/MM simulations, and important features of Quantum Chemistry calculations.
Introduces the OSSCAR framework for interactive quantum mechanics simulations, showcasing current use cases and specific examples like Monte Carlo simulations and 2D diffusion.
