分子軌道エネルギーを用いた機械学習によるエントロピーの予測

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/c...

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Bibliographic Details
Published inJournal of Computer Chemistry, Japan Vol. 22; no. 2; pp. 31 - 33
Main Authors 結城, 敬史, 中原, 和加奈, 寺前, 裕之
Format Journal Article
LanguageJapanese
Published 日本コンピュータ化学会 2023
Subjects
entropy
explanatory variables
machine learning
molecular orbital energy
prediction of entropy
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Summary: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.
ISSN:1347-1767
1347-3824
DOI:10.2477/jccj.2023-0025