Density Functional Theory

Density Functional Theory (DFT) is a quantum mechanical method used to predict the properties of materials and molecules, but its accuracy is limited by approximations in its core exchange-correlation functional. Current research heavily focuses on improving DFT's accuracy and efficiency through machine learning, employing neural networks (including graph neural networks and deep equilibrium models) to learn and refine these functionals or directly predict DFT Hamiltonians and other properties. These advancements are significantly impacting materials science and chemistry by accelerating materials discovery, enabling high-throughput screening, and improving the accuracy of simulations for diverse applications.