Machine-learning to predict anharmonic frequencies: a study of models and transferability-Reference-Cited by-同舟云学术

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Machine-learning to predict anharmonic frequencies: a study of models and transferability

Published:2024 Issue:35 Volume:26 Page:23495-23502
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Khanifaev Jamoliddin¹^ORCID,Schrader Tim¹^ORCID,Perlt Eva¹^ORCID

Affiliation:

1. Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena, Germany

Abstract

A machine learning algorithm predicts vibrational frequencies that are much closer to VSCF-calculated anharmonic frequencies compared to the harmonic approximation.

Funder

TURBOMOLE

Carl-Zeiss-Stiftung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/CP/D4CP01789G

Reference60 articles.

1. FTIR-spectroscopy of molecular clusters in pulsed supersonic slit-jet expansions

2. Vibrational Spectroscopy of Ionic Liquids

3. P. M. A.Sherwood , Vibrational Spectroscopy of Solids , CUP Archive , 1972

4. At the solid/liquid interface: FTIR/ATR — the tool of choice

5. Computational molecular spectroscopy

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