Simple and high-precision DFT-QSPR prediction of enthalpy of combustion for sesquiterpenoid high-energy–density fuels. (15th January 2023)

• Record Type: Journal Article
• Authors: Yang, Hang; Yang, Zhi-Jiang; Yang, Qi-Fan; Wei, Xin-Miao; Yuan, Yu-Quan; Wang, Liang-Liang; Hu, Yan-Fei; Ding, Jun-Jie
• Is Part Of: Fuel. Volume 332(2023)Part 2
• Publication Date: 2023-01-15
• Subjects: Sesquiterpenoid -- DFT -- Multiple corrections -- Enthalpy of combustion -- QSPR; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.126157
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: This study used sesquiterpenoids with 97 carbon skeletons for model construction. The scheme based on the DFT method combined with triple correction can accurately calculate Δ c H of sesquiterpenoid HEDFs. We developed the MLR equation with only 4 features for predicting Δ c H for sesquiterpenoid HEDFs. The proposed DFT-QSPR scheme can effectively guide the design of novel sesquiterpenoid HDEFs. Abstract: Sesquiterpenoids that are renewable and have high energy density and low freezing point are promising biomass high-energy–density fuels (HEDFs). The enthalpy of combustion ( Δ c H ) is the key characteristic to measure the heat energy content of HEDFs. Its accurate theoretical prediction and evaluation is essential for designing novel sesquiterpenoid HEDFs. Combining the screened and optimized density functional theory (DFT) method with triple computational correction, we presented a standardized calculation scheme for Δ c H with an average absolute error of only 2.6 % by 295 structurally diverse sesquiterpenoid HEDFs. Then, the extreme gradient boosting (XGBoost) algorithm was used to determine the correlation between the calculated Δ c H and 54 quantum chemical (QC) descriptors and the shapley additive explanation (SHAP) method to elucidate the key factors affecting Δ c H . Lastly, a quantitative structure–property relationship (QSPR) prediction model with only four QC features was constructed by using the SHAP results via the multiple linear regression (MLR) algorithms. Notably, the model has excellent prediction performance, decreased complexity, and improved applicability. The coefficient of determination ( R 2 ) of the internal training set and external test set of the model are 0.957 and 0.956, while the root mean square error ( RMSE ) are 8.626 kcal/mol and 9.012 kcal/mol, respectively. The proposed DFT-QSPR (the ingenious combination of DFT and QSPR) scheme can effectively guide the design of Δ c H of novel sesquiterpenoid HDEFs.