Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction
Elliot H. E. Farrar, Matthew N. Grayson
Abstract
the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.
Topics & Concepts
Mechanism (biology)Computer scienceEmpirical modellingEmpirical researchQuality (philosophy)Machine learningArtificial intelligenceSimulationMathematicsPhysicsQuantum mechanicsStatisticsMachine Learning in Materials ScienceCatalysis and Oxidation ReactionsCO2 Reduction Techniques and Catalysts