Novel method for properties prediction of pure organic compounds using machine learning. Chorbngam, N., Chawuthai, R., & Anantpinijwatna, A. Computer Aided Chemical Engineering, 50:431–437, 2021. doi abstract bibtex In classical thermodynamic, the estimation method of pure compounds properties was based on Newtonian physics, which required experimental data. It is proven to be inadequate for the growing demand of the novel chemical synthesis. There were several studies on the prediction of the pure compound properties based on QSPR methods. However, the conventional group-contribution based methods predictive capability was limited by the available measured data. Therefore, this study aims to approach the property prediction with a novel statistical-based method. The proposed method is derived using supervised machine learning algorithms. The experimental data used to train and validate the models were collected from the published literature. These data set are composed of the alkanes, alkenes, and alkynes derivatives containing 1-12 carbon atoms. The results show the improved accuracy of the model prediction compare to the conventional method in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).
@article{chorbngam_novel_2021-1,
title = {Novel method for properties prediction of pure organic compounds using machine learning},
volume = {50},
issn = {1570-7946},
doi = {10.1016/b978-0-323-88506-5.50068-1},
abstract = {In classical thermodynamic, the estimation method of pure compounds properties was based on Newtonian physics, which required experimental data. It is proven to be inadequate for the growing demand of the novel chemical synthesis. There were several studies on the prediction of the pure compound properties based on QSPR methods. However, the conventional group-contribution based methods predictive capability was limited by the available measured data. Therefore, this study aims to approach the property prediction with a novel statistical-based method. The proposed method is derived using supervised machine learning algorithms. The experimental data used to train and validate the models were collected from the published literature. These data set are composed of the alkanes, alkenes, and alkynes derivatives containing 1-12 carbon atoms. The results show the improved accuracy of the model prediction compare to the conventional method in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).},
journal = {Computer Aided Chemical Engineering},
author = {Chorbngam, Nattasinee and Chawuthai, Rathachai and Anantpinijwatna, Amata},
year = {2021},
keywords = {Worldwide Standard},
pages = {431--437},
}
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