Accurate Prediction of ωB97X-D/6-31G* Equilibrium Geometries from a Neural Net Starting from Merck Molecular Force Field (MMFF) Molecular Mechanics Geometries.

Starting from Merck Molecular Force Field (MMFF) geometries, a neural-net based model has been formulated to closely reproduce ωB97X-D/6-31G* equilibrium geometries for organic molecules. The model involves training to >6 million energy and force calculations for molecules with molecular weights ranging from 200 to 600 amu, corresponding to both ωB97X-D/6-31G* and MMFF equilibrium geometries as well as small deviations away from these geometries. 422 natural products not involved in training with molecular weights ranging from 200 to 691 amu have been used to assess the neural net model against changes in bond lengths, bond angles, and dihedral angles, as well as against changes in proton and ¹³C chemical shifts resulting from using equilibrium geometries from the neural-net in lieu of geometries from ωB97X-D/6-31G*. The neural net reduces calculation times by two or more orders of magnitude.