QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.1.0

A quantum chemical (QC) dataset of optimization targets was generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train the parameters of the OpenFF 2.1.0 Sage forcefield. Additional details can be found in the GitHub dataset repository and the Force field repository. General Information * Date: 2025-05-22 * Purpose: Complete set of training data for OpenFF 2.1.0 Sage * Name: OpenFF SMIRNOFF Sage 2.1.0 * Submitter: Jennifer A Clark * Dataset curator: Pavan Behara * Class: OpenFF Optimization Dataset * Dataset Type: optimization * Number of unique molecules: 1701 * Number of filtered molecules: 0 * Number of conformers: 5580 * Number of conformers (min, mean, max): 1.00, 3.17, 17.00 * Molecular weight (min, mean, max): 16.04, 235.40, 544.64 * Charges: -3.0, -2.0, -1.0, 0.0, 1.0, 2.0 * Dataset generator: Chaya Stern, Hyesu Jang, Jessica Maat, and Pavan Behara * Class: OpenFF TorsionDrive Dataset * Number of unique molecules: 953 * Number of filtered molecules: 0 * Number of driven torsions: 1300 * Number of conformers: 974 * Number of conformers (min, mean, max): 1, 1, 3 * Molecular weight (min, mean, max): 32.04, 185.54, 503.42 * Charges: -1.0, 0.0, 1.0 * Dataset generator: Simon Boothroyd, John Chodera, Trevor Gokey, Hyesu Jang, Yudong Qiu, Bryon Tjanaka