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Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. The test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset. 

Dynamic effects are an important determinant of chemical reactivity and selectivity, but the deliberate manipulation of atomic motions during a chemical transformation is not straightforward. Here, we demonstrate that extrinsic force exerted upon cyclobutanes by stretching pendant polymer chains influences product selectivity through force-imparted nonstatistical dynamic effects on the stepwise ring-opening reaction. The high product stereoselectivity is quantified by carbon-13 labeling and shown to depend on external force, reactant stereochemistry, and intermediate stability. Computational modeling and simulations show that, besides altering energy barriers, the mechanical force activates reactive intramolecular motions nonstatistically, setting up “flyby trajectories” that advance directly to product without isomerization excursions. A mechanistic model incorporating nonstatistical dynamic effects accounts for isomer-dependent mechanochemical stereoselectivity.