Ground State

Determining the ground state—the lowest energy state—of quantum many-body systems is a fundamental challenge in physics and chemistry, crucial for understanding material properties and predicting molecular behavior. Current research focuses on developing efficient machine learning algorithms, including neural networks (like FermiNet and KineticNet) and kernel methods, to approximate ground states and their properties, often leveraging symmetries and locality to improve accuracy and reduce computational cost. These advancements are significantly impacting fields like materials science and quantum chemistry by enabling more accurate and efficient simulations of complex systems, leading to improved predictions of molecular properties and the discovery of novel materials.