title: Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.
abstract: Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions.
authors.affiliation: University of California, Irvine , Department of Computer Science, Irvine, California 92697, United States.
authors.forename: Peter
authors.initials: P
authors.lastname: Sadowski
authors.affiliation: University of California, Irvine , Department of Computer Science, Irvine, California 92697, United States.
authors.forename: David
authors.initials: D
authors.lastname: Fooshee
authors.affiliation: ExxonMobil Research and Engineering , Annandale, New Jersey 08801, United States.
authors.forename: Niranjan
authors.initials: N
authors.lastname: Subrahmanya
authors.affiliation: University of California, Irvine , Department of Computer Science, Irvine, California 92697, United States.
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
mesh_terms: D000069550:Machine Learning; D008958:Models, Molecular; D008968:Molecular Conformation; D011789:Quantum Theory; D013816:Thermodynamics