Kohn Sham

Kohn-Sham density functional theory (KS-DFT) is a cornerstone of electronic structure calculations, but its computational cost limits scalability for large systems. Current research focuses on accelerating KS-DFT through machine learning, employing neural networks (including graph transformers and convolutional architectures) to learn relationships between electron density and system properties, or to directly approximate components of the KS equations like the Hamiltonian or kinetic energy functional. These advancements aim to improve the accuracy and efficiency of electronic structure predictions, impacting fields like materials science, drug discovery, and the study of radiation effects in materials. The development of universal models applicable across diverse chemical systems and the integration of machine learning with existing quantum chemistry methods are key themes.