Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions
Javier Emilio Alfonso Ramos, Rebecca M. Neeser, Thijs Stuyver
Abstract
In this work, we explore how existing datasets of quantum chemical properties can be repurposed to build data-efficient downstream ML models, with a particular focus on predicting the activation energy of hydrogen atom transfer reactions.
Topics & Concepts
Hydrogen atomQuantum chemicalRepurposingComputer scienceFocus (optics)Atom (system on chip)QuantumOn the flyWork (physics)Transfer (computing)HydrogenChemical reactionChemistryBiological systemMoleculePhysicsThermodynamicsQuantum mechanicsEngineeringOrganic chemistryBiologyOperating systemParallel computingOpticsEmbedded systemAlkylWaste managementMachine Learning in Materials ScienceComputational Drug Discovery MethodsMetabolomics and Mass Spectrometry Studies