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Machine learning approach for describing vibrational solvatochromism

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Title
Machine learning approach for describing vibrational solvatochromism
Author(s)
Kwac, Kijeong; Cho, Minhaeng
Subject
MOLECULAR-STRUCTURE DISTORTIONS, ; IR SPECTROSCOPY, ; LINE-SHAPES, ; CHEMISTRY, ; DYNAMICS, ; PEPTIDE
Publication Date
2020-05
Journal
The Journal of chemical physics, v.152, no.17, pp.174101
Publisher
AIP Publishing
Abstract
© 2020 Author(s). Machine learning is becoming a more and more versatile tool describing condensed matter systems. Here, we employ the feed-forward and the convolutional neural networks to describe the frequency shifts of the amide I mode vibration of N-methylacetamide (NMA) in water. For a given dataset of configurations of an NMA molecule solvated by water, we obtained comparable or improved results for describing vibrational solvatochromic frequency shift with the neural network approach, compared to the previously developed differential evolution algorithm approach. We compared the performance of the atom centered symmetry functions (ACSFs) and simple polynomial functions as descriptors for the solvated system and found that the polynomial function performs better than the ACSFs employed in the description of the amide I vibrational solvatochromism
URI
https://pr.ibs.re.kr/handle/8788114/8616
DOI
10.1063/5.0005591
ISSN
0021-9606
Appears in Collections:
Center for Molecular Spectroscopy and Dynamics(분자 분광학 및 동력학 연구단) > 1. Journal Papers (저널논문)
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