2026-10-11T15:43:07Z https://www.scidb.cn/oai

10.57760/sciencedb.37834 2026-06-08T11:36:11Z

2026-06-08 Data for: Neural network self-consistent fields for density functional theory doi:10.57760/sciencedb.37834 en This dataset supports the manuscript "Neural network self-consistent fields for density functional theory". It contains the data generated and used in the study of NeuralSCF, a machine-learning framework that models the Kohn-Sham density map and emulates self-consistent field iterations to predict ground-state electron densities and derived electronic-structure properties. The study evaluates NeuralSCF on molecular datasets including QM9, MD17/MD22 trajectories, BFDb-SSI non-covalent systems, bond-rotation examples, and larger QMugs molecules, using PySCF calculations with the cc-pVTZ basis, PBE exchange-correlation functional, and cc-pVTZ-AutoAux density representation. Source data for the figures and related computational outputs are included for reproducibility. density functional theory; DFT; quantum; machine learning; electronic structures Feitong Song Ji Feng PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1038/s41524-026-02110-0 Science Data Bank