Supporting data/code for "Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds"

This repository contains the datasets and analysis scripts used in our machine-learning (ML) based quantum chemistry study. The data and code are provided to ensure full reproducibility of the results reported in the associated manuscript. Dataset: Training datasets: Includes training data for all molecules studied, stored in a structured and compact HDF5 format. Testdata: Contains the complete test dataset used for evaluating the ML models. Scripts/Code: RMSE analysis: Compute and analyze the root mean square error (RMSE) values for energies, dipoles, forces, and other properties. Learning curves: Scripts and data for plotting the learning curves along training set sizes for some selected molecules. HOMO-LUMO Gap: Scripts and data for computing and plotting the HOMO-LUMO gaps. Force correction analysis: Scripts and data to compute and plot the force errors before and after correction. AIMD analysis: Scripts and data for analyzing ab initio molecular dynamics (AIMD) trajectories. Cost: Scripts and data for plotting both the training cost and AIMD cost.