Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
Yunsie Chung, William H. Green
Abstract
, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.
Topics & Concepts
SolvationSolventChemistrySolvent effectsQuantum chemistryAtom (system on chip)Reaction rateQuantumComputational chemistryQuantum chemicalReaction mechanismComputer scienceOrganic chemistryMoleculeCatalysisPhysicsQuantum mechanicsParallel computingMachine Learning in Materials ScienceComputational Drug Discovery MethodsVarious Chemistry Research Topics