Energy Function

Energy functions, mathematical representations of a system's state and its associated energy, are central to numerous scientific fields, with the primary objective of accurately modeling and predicting system behavior. Current research focuses on improving the accuracy and efficiency of energy function estimation using machine learning techniques, including neural networks, particularly for applications in density functional theory and molecular dynamics simulations. These advancements are impacting diverse areas, from materials science (e.g., molecular design) and quantum chemistry (e.g., Schrödinger equation solutions) to Bayesian inference and the development of more physically accurate simulations. The development of more efficient and accurate energy functions promises to significantly accelerate scientific discovery and technological innovation across multiple disciplines.