Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767
Letters
Prediction of Entropy by Machine Learning with Molecular Orbital Energies
Takafumi YUUKI,Wakana NAKAHARAHiroyuki TERAMAE
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2023 Volume 22 Issue 2 Pages 31-33

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Abstract

The values of the entropy of 148 small organic molecules have been estimated by machine learning with only molecular orbital energies as the explanatory variables. Out of 148 molecules,we used 104 molecules for the training set and 44 molecules for the test set. We used 139 regression methods of R/caret package for machine learning. We evaluated values by RMSE (Root Mean Squared Error) and R2 (coefficient of determination). From those evaluation,xgbLinear (eXtreme Gradient Boosting) and RRFglobal (Regularized Random Forest) are considered better than other regression methods. It has been proved that the entropy can be predicted by the molecular orbital energies only.

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© 2023 Society of Computer Chemistry, Japan
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