Free Energy
Free energy, a fundamental concept in thermodynamics and statistical mechanics, is being actively investigated across diverse scientific fields to understand complex systems' behavior and predict their properties. Current research focuses on developing and applying advanced computational methods, including graph neural networks, normalizing flows, and variational inference techniques, to estimate free energy landscapes and efficiently sample high-dimensional systems. These advancements are improving the accuracy and efficiency of free energy calculations in areas such as materials science, drug design, and artificial intelligence, enabling more precise predictions and deeper insights into complex phenomena.
Papers
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model
Yikai Liu, Tushar K. Ghosh, Guang Lin, Ming Chen
Modeling arousal potential of epistemic emotions using Bayesian information gain: Inquiry cycle driven by free energy fluctuations
Hideyoshi Yanagisawa, Shimon Honda