Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water

Abstract

Second-Harmonic Scatteringh (SHS) experiments provide a unique approach to probe non-centrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells and tissue. A central assumption made in analyzing SHS experiments is that the each molecule scatters light according to a constant molecular hyperpolarizability tensor ${\mathbf{\beta }}^{(2)}$. Here, we investigate the dependence of the molecular hyperpolarizability of water on its environment and internal geometric distortions, in order to test the hypothesis of constant ${\mathbf{\beta }}^{(2)}$. We use quantum chemistry calculations of the hyperpolarizability of a molecule embedded in point-charge environments obtained from simulations of bulk water. We demonstrate that both the heterogeneity of the solvent configurations and the quantum mechanical fluctuations of the molecular geometry introduce large variations in the non-linear optical response of water. This finding has the potential to change the way SHS experiments are interpreted: in particular, isotopic differences between H${}_2$O and D${}_2$O could explain recent second-harmonic scattering observations. Finally, we show that a simple machine-learning framework can predict accurately the fluctuations of the molecular hyperpolarizability. This model accounts for the microscopic inhomogeneity of the solvent and represents a first step towards quantitative modelling of SHS experiments.

References in corpus (6)

• Canonical sampling through velocity-rescaling
• On representing chemical environments
• Competing quantum effects in the dynamics of a flexible water model
• Efficient stochastic thermostatting of path integral molecular dynamics
• High Order Path Integrals Made Easy
• Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids

Cited by in corpus (9)

• Physics-inspired structural representations for molecules and materials
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• A Transferable Machine-Learning Model of the Electron Density
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
• Mean-Field Theory of Water-Water Correlations in Electrolyte Solutions
• A simple approach to rotationally invariant machine learning of avector quantity
• A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra