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An atomistic approach for modeling of polarizability and Raman scattering of water clusters and liquid water
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In this work, we develop a framework for atomistic modeling of electronic polarizability to predict the Raman spectra of hydrogen-bonded clusters and liquids from molecular dynamics (MD) simulations. The total polarizability of the system is assumed to arise from contributions of both the monomer unit and intermolecular interactions. The generalized bond-polarizability model (GBPM), inspired by the classic bond-polarizability model, effectively describes the electronic polarizability of a monomer. To account for the electronic polarizability arising from intermolecular interactions, we use a basis set of rapidly decaying functions of interatomic distances. We apply this model to calculate the electronic polarizability and Raman spectra of water clusters ((H2O)r, r = 2, 3, 4, 5, 6) and liquid water. The computational results are compared with the results of quantum-mechanical calculations for clusters and to experimental data for the liquid. It is demonstrated that this simple and physically motivated model, which relies on a small number of parameters, performs well for clusters at both low and high temperatures, capturing strong anharmonic effects. Moreover, its high transferability suggests its applicability to other water clusters. These results suggest that a hierarchical approach based on the Jacob's ladder of increasingly sophisticated and accurate atomistic polarizability models incorporating additional effects can be used for efficient modeling of Raman spectra from MD simulations of clusters, liquids and solids.
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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.
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