Even with significantly fewer parameters, the OpenFF family of FFs has been shown to offer competitive accuracy when benchmarked against QM geometric and energetic properties . However, torsion parameters, in particular, are known to be particularly sensitive to the local environment within the target molecule and may be expected to be less transferable than the other valence parameters. Torsional parameters must effectively account for many stereoelectronic and steric effects . In addition, resonance effects between aromatic rings, for example, can mean that even non-local substitutions, which may not be captured via chemical perception, can affect torsional profiles . Figure compares example potential energy surfaces of two molecular fragments calculated with contemporary general force fields OpenFF 2.0.0 (Sage) and GAFF 2.11, with a QM reference (see Methods). While the default, transferable torsional parameters show good performance in some cases (top panel), more complex chemical environments can lead to an inaccurate reproduction of the QM potential energy surface (lower panel), resulting from poor transferability. Thus, due to the complexity encoded in torsional parameters, and the resulting poor or partial transferability, they are often the target for reparametrization. To this end, several automated methods exist to derive torsion parameters that are specific to the target molecule under study. For example, an automated torsion parametrization package, named FFBuilder supplements the already extensive set of base library parameters in the proprietary OPLS3 FF . This allows users to fit new torsion parameters for novel chemistry that is poorly represented by the general FF using a consistent parametrization method. Several other tools also aid the fitting of bespoke torsion parameters to QM potential energy surfaces; these include QUBEKit , paramol , parmfit , qforce , JOYCE , DFFR , Rotational Profiler , or the algebraic method of Kania , to name a few. Although bespoke torsion parameters have the potential to improve the accuracy of molecular simulations, fitting these parameters to multiple QM torsion scans can significantly slow down the parameter assignment stage for users. However, there is now the opportunity to make use of recent advances in machine learning (e.g. ANI ) and semi-empirical (e.g. xTB )