Machine learning approach for describing vibrational solvatochromism

Kijeong Kwac, Minhaeng Cho

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Article number174101
JournalJournal of Chemical Physics
Issue number17
Publication statusPublished - 2020 May 7

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry


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