Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions
(2022)
Journal Article
Graham, R. S., & Wheatley, R. J. (2022). Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions. Chemical Communications, 58(49), 6898-6901. https://doi.org/10.1039/d2cc01820a
Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately in... Read More about Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions.