Predicting phosphorescence energies and inferring wavefunction localization with machine learning
Andrew E. Sifain, Levi Lystrom, Richard A. Messerly, Justin S. Smith, Benjamin Nebgen, Kipton Barros, Sergei Tretiak, Nicholas Lubbers, Brendan J. Gifford
Abstract
spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model.
Topics & Concepts
PhosphorescenceSinglet stateWave functionEnergy (signal processing)MoleculePhysicsChemical physicsMolecular physicsComputer scienceAtomic physicsQuantum mechanicsFluorescenceExcited stateMachine Learning in Materials ScienceInnovative Microfluidic and Catalytic Techniques InnovationElectronic and Structural Properties of Oxides