Infrared spectra of neutral polycyclic aromatic hydrocarbons based on machine learning potential energy surface and dipole mapping
Gaétan Laurens, Malalatiana Rabary, Julien Lam, Daniel Peláez, Abdul-Rahman Allouche
Topics & Concepts
TransferabilityDipolePotential energy surfaceInfrared spectroscopyPerturbation theory (quantum mechanics)InfraredArtificial neural networkArtificial intelligenceComputer scienceChemistryMachine learningComputational chemistryQuantumSurface (topology)Energy (signal processing)Perturbation (astronomy)Potential energySpectroscopySpectral lineQuantum chemistryStatistical physicsQuantum chemicalBiological systemChemical physicsComputational physicsMaterials scienceMolecular physicsAlgorithmWork (physics)Discrete dipole approximationAb initioMachine Learning in Materials ScienceAdvanced Physical and Chemical Molecular InteractionsSynthesis and Properties of Aromatic Compounds