Lecture
This lecture explores the applications of machine learned interatomic potentials in predicting properties such as energy, dipole moment, and polarizability. It discusses the use of neural networks to represent high-dimensional potential-energy surfaces, providing accurate descriptions of chemical processes. The lecture also covers the atomic representations and machine learning architectures used to optimize the accuracy of predictions, showcasing the potential of machine learned potentials in accurately capturing physical details at a fraction of the cost of traditional methods.