Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Raman spectrum and polarizability of liquid water from deep neural networks
Ist Teil von
Physical chemistry chemical physics : PCCP, 2020-05, Vol.22 (19), p.1592-162
Ort / Verlag
England: Royal Society of Chemistry
Erscheinungsjahr
2020
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
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 H
2
O and D
2
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.
Using deep neural networks to model the polarizability and potential energy surfaces, we compute the Raman spectrum of liquid water at several temperatures with
ab initio
molecular dynamics accuracy.