Machine learning models can compute the polarizability time series needed for MD-Raman spectra at a small fraction of DFT cost, making finite-temperature Raman prediction for anharmonic materials practical.
Quarti , author E
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Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
Machine learning models can compute the polarizability time series needed for MD-Raman spectra at a small fraction of DFT cost, making finite-temperature Raman prediction for anharmonic materials practical.