The infrastructure used to generate Parsley takes an initial force field as its starting point and optimizes it against reference training data to create an optimized force field, which in turn is benchmarked against test-set data prior to release (Figure ). The software part of this infrastructure comprises a toolkit that assigns force field parameters to molecules of interest (openff toolkit); a component that computes a set of target properties for a set of input molecules (openff evaluator); and the ForceBalance code, which uses the openff evaluator to optimize force field parameters against the selected reference data. In general, the reference dataset can include both quantum chemical and experimental data. The Parsley force field was generated by refitting the parameters in the valence terms of the initial SMIRNOFF99Frosst force field against an extensive new set of high-level quantum mechanical data, which include energies, gas-phase geometries, and other properties. Note that the starting parameters in SMIRNOFF99Frosst in turn originated from two parent force fields, AMBER parm99 42 and Merck's parm@Frosst, which had been parameterized to reproduce gas phase geometries and energetics computed at lower levels of QM than that used here, for selected molecules. Here, Section 2.1 details the force field parameters that were optimized, the QM dataset used to drive the optimization, and the application of ForceBalance to carry out the optimization from the SMIRNOFF99Frosst 35 starting point. Section 2.2 then describes how the resulting Parsley force field was tested against benchmark data outside the training sets, comprising gas phase properties from QM calculations, measured liquid-state properties, and measured protein-ligand binding free energies.