Low Energy

Low-energy research focuses on efficiently finding and characterizing low-energy states within complex systems, spanning from fundamental physics to materials science and molecular dynamics. Current approaches leverage machine learning, particularly neural networks (like convolutional and transformer architectures) and symbolic regression, to analyze high-dimensional data, build accurate predictive models, and optimize sampling techniques for exploring these low-energy landscapes. These advancements enable faster and more accurate analyses of complex systems, improving our understanding of phenomena ranging from particle physics to protein folding and nanocluster stability, and facilitating the design of novel materials and technologies.