Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra-Reference-Cited by-同舟云学术

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Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Published:2024-04-03 Issue:15 Volume:128 Page:6464-6470
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Grumet Manuel¹,von Scarpatetti Clara¹^ORCID,Bučko Tomáš²³^ORCID,Egger David A.¹^ORCID

Affiliation:

1. Physics Department, TUM School of Natural Sciences, Technical University of Munich, Garching 85748, Germany

2. Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava SK-84215, Slovakia

3. Institute of Inorganic Chemistry, Slovak Academy of Sciences, Bratislava SK-84236, Slovakia

Funder

Bundesministerium f?r Bildung und Forschung

FP7 People: Marie-Curie Actions

Gauss Centre for Supercomputing

Bayerisches Staatsministerium f?r Wissenschaft und Kunst

Alexander von Humboldt-Stiftung

Agent?ra na Podporu V?skumu a V?voja

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c00886

Reference60 articles.

1. Raman Techniques: Fundamentals and Frontiers

2. Surface- and Tip-Enhanced Raman Spectroscopy in Catalysis

3. Raman spectroscopy as a powerful tool to characterize ceria-based catalysts

4. New advances in using Raman spectroscopy for the characterization of catalysts and catalytic reactions

5. A Review on the Application of In-Situ Raman Spectroelectrochemistry to Understand the Mechanisms of Hydrogen Evolution Reaction

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1. Boosting the Modeling of Infrared and Raman Spectra of Bulk Phase Chromophores with Machine Learning;Journal of Chemical Theory and Computation;2024-08-15

2. Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities;Journal of Chemical Information and Modeling;2024-06-03

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