Raman spectrum and polarizability of liquid water from deep neural networks
Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Abstract
O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.
Topics & Concepts
PolarizabilityArtificial neural networkAb initioRaman spectroscopyIntermolecular forceIntramolecular forceMolecular physicsRepresentation (politics)Raman scatteringAb initio quantum chemistry methodsChemistryStatistical physicsDensity functional theorySpectral lineEnergy (signal processing)Computational physicsChemical physicsApproximation errorComputational chemistryResolution (logic)Electronic structureLiquid waterMolecular dynamicsMaterials scienceSpectrum (functional analysis)Scheme (mathematics)Potential energyPhysicsMachine Learning in Materials ScienceSpectroscopy and Quantum Chemical StudiesMaterial Dynamics and Properties