Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Introduces path integral molecular dynamics and its applications in quantum mechanics, focusing on nuclear quantum effects and their implications for molecular simulations.
