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O-kRystal, a periodic E(3)-equivariant graph neural network, learns corrections to electronic states beyond DFT, achieving HSE06 band predictions across nearly 6,000 materials, G0W0 quasiparticle energies in 2D systems, and a 68.7% reduction in band energy MAE on strongly correlated materials.
MALOQ introduces a massively accelerated machine learning model for predicting density functional theory Hamiltonian/density matrices, enabling electronic-structure calculations for systems with up to 100k atoms using an SO(2)-equivariant backbone and scalable graph distribution, achieving over 30% time-per-epoch reduction on the Alps supercomputer.