Energy Landscape

Energy landscape theory explores the relationship between a system's configuration and its energy, revealing how this relationship governs the system's behavior and evolution. Current research focuses on applying energy landscape concepts to diverse fields, including machine learning (optimizing neural network training and interpreting model decisions), robotics (controlling locomotion and grasping), and materials science (designing and discovering new materials). These applications leverage various techniques, such as graph neural networks, stochastic gradient descent, and potential energy map-based analyses, to navigate complex energy landscapes and achieve desired outcomes. The insights gained are crucial for improving algorithm efficiency, enhancing robotic capabilities, and accelerating materials discovery.