QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.0.0

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 2.0.0 Sage force field. This Generation 2 dataset increases chemical diversity when compared to Generation 1, which are of value to our industry partners. Large molecules (>20 heavy atoms) were also included, including more flexible molecules and a greater degree of conformational variation which provide intramolecular interactions.

Further information can be found in the GitHub repository of these compiled datasets and the OpenFF 2.0.0 Sage force field repository.

General Information

Name: OpenFF SMIRNOFF Sage 2.0.0Purpose: Complete set of training data for OpenFF 2.0.0 SageDataset Submitter: Jennifer A. ClarkDataset Curator: Simon BoothroydDataset Generator: Jessica Maat and Hyesu JangClass: OpenFF Optimization DatasetDataset Type: optimizationNumber of unique molecules:   1039Number of filtered molecules: 0 Number of conformers:         3663Number of conformers (min mean max): 1.00, 3.53, 10.00Mean molecular weight: 261.37Max molecular weight: 544.64Set of charges: -2.0, -1.0, 0.0, 1.0Class: OpenFF TorsionDrive DatasetDataset Type: torsiondriveNumber of unique molecules: 562Number of filtered molecules: 0Number of driven torsions: 713Number of conformers: 563Number of conformers (min, mean, max): 1, 1, 2Molecular weight (min, mean, max): 46.07, 224.91, 503.42Charges: -1.0, 0.0, 1.0