The computational modeling of chemical and biological systems relies on an accurate assessment of the energetics and geometries of the systems. Methods range from more accurate, higher-cost quantum mechanical (QM) techniques to more approximate but efficient methods such as classical mechanics-based calculations. The latter have the advantage of being applicable to larger systems over longer timescales. Extended simulation timescales are particularly relevant for the calculation of thermodynamic properties such as protein-ligand binding affinity where the accurate treatment of entropy, desolvation, and other factors requires ensemble-based free energy approaches. The classical mechanical calculations use a force field that gives the energy of the system as a function of the coordinates, given a number of empirical parameters fit to describe this and other properties accurately. Force field development has an extensive history, and the approaches taken vary with respect to the chemical space covered, the data used for training, and the approach for parameter optimization. It is common to fit force fields using data from both experimental physical property measurements and reference QM calculations carefully chosen to represent the systems for which the force field is designed. The performance of the resulting force field is then assessed by its ability to reproduce either experimental observables, or QM data such as geometries and relative energies. Given that force field development is complex due to the diversity of training data, various functional forms, and approaches to chemical perception, it is expected that the resulting force fields vary in how accurately they reproduce the properties of interest. Force fields for proteins, based on the 20 common amino acids, have been refined over many years and are widely used and continue to be updated. A high quality general force field suitable for small, organic and drug-like molecules represents a bigger challenge due to the vast chemical space that must be considered. Furthermore, inherent to innovation is the search for novel chemical matter and a general force field should be suitable for application to as-yet undiscovered compounds. Popular current small molecule force fields include the General AMBER force field (GAFF), the CHARMM General force field (CGenFF), and the Optimized Potentials for Liquid Simulations force field (OPLS). These approaches have undergone substantial improvement, with the latest versions of OPLS in particular showing impressive performance, but it is widely accepted that further improvements are possible. Begun in late 2018, the Open Force Field (OpenFF) Initiative is a relatively new effort to build and optimize a general force field using an automated and reproducible procedure, with all software, data, and workflows made freely available. Rather than traditional atom-typing, the approach builds on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which can incorporate increased chemical diversity without needlessly increasing the complexity of the underlying specification and parameterization.