Electronic Structure

Electronic structure research aims to computationally determine the arrangement and energies of electrons in atoms and molecules, providing fundamental insights into material properties. Current efforts heavily utilize machine learning, employing neural networks (including graph transformers, convolutional neural networks, and deep equilibrium models) to accelerate calculations and improve the accuracy of density functional theory (DFT) and other methods, often focusing on Hamiltonian prediction and reduced density matrix calculations. These advancements significantly impact materials science, chemistry, and related fields by enabling faster and more accurate simulations of complex systems, facilitating high-throughput screening for novel materials and accelerating the design of new technologies.