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Author Vermeyen, T.; Brence, J.; Van Echelpoel, R.; Aerts, R.; Acke, G.; Bultinck, P.; Herrebout, W.
Title Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism Type A1 Journal article
Year (down) 2021 Publication Physical Chemistry Chemical Physics Abbreviated Journal Phys Chem Chem Phys
Volume 23 Issue 35 Pages 19781-19789
Keywords A1 Journal article; AXES (Antwerp X-ray Analysis, Electrochemistry and Speciation); Molecular Spectroscopy (MolSpec)
Abstract The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Wos 000691366500001 Publication Date 2021-08-25
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1463-9076; 1463-9084 ISBN Additional Links UA library record; WoS full record; WoS citing articles
Impact Factor 4.123 Times cited Open Access OpenAccess
Notes Approved Most recent IF: 4.123
Call Number UA @ admin @ c:irua:180290 Serial 7951
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