QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.2.0

Description A quantum chemical (QC) optimization and torsiondrive datasets generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train parameters in OpenFF 2.2.0 Sage with improved small ring internal angles and sulfamide geometries. Additional details can be found in the GitHub dataset repository and the Force field repository. General Information * Date: 2025-05-23 * Purpose: Complete set of training data for OpenFF 2.2.0 Sage * Name: OpenFF SMIRNOFF Sage 2.2.0 * Dataset submitter: Jennifer A Clark * Dataset curator: Pavan Behara * Class: OpenFF Optimization Dataset * Dataset Type: optimization * Number of unique molecules: 1691 * Number of filtered molecules: 0 * Number of conformers: 5126 * Number of conformers (min, mean, max): 1.00, 3.03, 12.00 * Molecular weight (min, mean, max): 16.04, 236.01, 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 * Dataset Type: torsiondrive * Number of unique molecules: 956 * Number of filtered molecules: 0 * Number of driven torsions: 1290 * Number of conformers: 982 * Number of conformers (min, mean, max): 1, 1, 3 * Molecular weight (min, mean, max): 46.07, 185.48, 503.42 * Charges: -1.0, 0.0, 1.0 * Dataset generator: Simon Boothroyd, John Chodera, Trevor Gokey, Hyesu Jang, Yudong Qiu, Bryon Tjanaka