Issue 19, 2020

Raman spectrum and polarizability of liquid water from deep neural networks

Abstract

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H2O and D2O. 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.

Graphical abstract: Raman spectrum and polarizability of liquid water from deep neural networks

Article information

Article type
Paper
Submitted
08 abr 2020
Accepted
30 abr 2020
First published
30 abr 2020

Phys. Chem. Chem. Phys., 2020,22, 10592-10602

Author version available

Raman spectrum and polarizability of liquid water from deep neural networks

G. M. Sommers, M. F. Calegari Andrade, L. Zhang, H. Wang and R. Car, Phys. Chem. Chem. Phys., 2020, 22, 10592 DOI: 10.1039/D0CP01893G

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements