Predicting entropy and heat capacity of hydrocarbons using machine learning
نویسندگان
چکیده
Chemical substances are essential in all aspects of human life, and understanding their properties is for developing chemical systems. The species can be accurately obtained by experiments or ab initio computational calculations; however, these time-consuming costly. In this work, machine learning models (ML) estimating entropy, S, constant pressure heat capacity, Cp, at 298.15 K, developed alkanes, alkenes, alkynes. training data entropy capacity collected from the literature. Molecular descriptors generated using alvaDesc software used as input features ML models. Support vector regression (SVR), v-support (v-SVR), random forest (RFR) algorithms were trained with K-fold cross-validation on two levels. first level assessed models' performance, second final Between three chosen, SVR shows better performance test dataset. model was then compared against traditional Benson's group additivity to illustrate advantages model. Finally, a sensitivity analysis performed find most critical property estimations.
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ژورنال
عنوان ژورنال: Energy and AI
سال: 2021
ISSN: ['2666-5468']
DOI: https://doi.org/10.1016/j.egyai.2021.100054