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A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

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Frontiers of Chemical Science and Engineering 2022年第2期16卷 152-167页

作者： Qilei Liu Yinke Jiang Lei Zhang Jian Du Institute of Chemical Process Systems Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased *** life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste *** these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product *** this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so *** workflow of framework consists of three *** the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third *** whole framework has been carried out within a molecular property prediction *** case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

关键词： molecular property quantum mechanics quantitative structure-property relationship heat of reaction solid-liquid phase equilibrium

Prediction on Critical Micelle Concentration of Nonionic Surfactants in Aqueous Solution:quantitative structure-property relationship Approach

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Chinese Journal of Chemistry 2003年第12期21卷 1573-1579页

作者：王正武黄东阳宫素萍李干佐 Shantou University Medical College Shangdong Electric Power Central Hospital Key Lab of Colloid and Interface Chemistry for State Education Ministry Shandong University

In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.

关键词： quantitative structure-property relationship critical micelle concentration nonionic surfactant

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

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Frontiers of Environmental Science & Engineering 2014年第5期8卷 683-692页

作者： Davoud BEIKNEJAD Mohammad Javad CHAICHI Faculty of Chemistry University of Mazandaran Babolsar 47416-95447 Iron

In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- property relationship （QSPR） study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression （MLR） and partial least squares （PLS） for the prediction of the photolysis half-life （t1/2） of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital （EHoMO）, largest electron density of an atom in the molecule （ED^＋） and lipophilicity （logP）. While in the model derived from PLS, besides aforementioned EHOMO and ED^＋ descriptors, the molecular surface area （Sm）, molecular weight （M-W）, electronegativity （X）, energy of the second highest occupied molecular orbital （EHoMO- 1） and dipole moment （μ） had dominant effects on logt1/2 values of dyes. These were applicable for all classes of studied dyes （including monoazo, disazo, oxazine, sulfo- nephthaleins and derivatives of fluorescein）. The results were also assessed for their consistency with findings from other similar studies.

关键词： dye photolysis half-life quantitative structure-property relationship continuous-flow stepwise multiple linear regression partial least squares

Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach

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Journal of Thermal Science 2023年第2期32卷 521-530页

作者： CHEN Zhuo VOM LEHN Florian PITSCH Heinz CAI Liming School of Automotive Studies Tongji UniversityShanghai 201804China Institute for Combustion Technology RWTH Aachen UniversityAachen 52056German

A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives,for which the knowledge of the fuels’characteristic properties forms the *** aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes(YSI).In conjunction with our previously developed database for gasoline compounds,which includes the physical and chemical properties,such as octane numbers,laminar burning velocity,and heat of vaporization,for more than 600 species,the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition *** this purpose,a quantitative structure-property relationship(QSPR)model is developed to predict the YSI of fuel species by using artificial neural network(ANN)techniques with 21 well-selected functional group descriptors as input *** model is trained and cross-validated with the YSI database reported by Yale *** is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular *** addition,the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these ***,a selection of potential gasoline blending components is carried out exemplarily,by taking the fuels’potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI,respectively.

关键词： yield sooting index gasoline replacements and additives quantitative structure-property relationship artificial neural network

Studies on the Cloud Points of Nonionic Surfactants with QSPR

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Chemical Research in Chinese Universities 2007年第6期23卷 715-719页

作者： CHEN Mei-ling WANG Zheng-wu ZHANG Ge-xin GU Jin CUN Zhe TAO Fu-ming School of Chemistry and Materials Engineering Southern Yangtze University Wuxi 214122 P. R. China Department of Food Science ＆ Technology School of Agriculture and Biology Shanghai Jiaotong University Shanghai 201101 P. R. China Department of Chemistry and Biochemistry California State University Fullerton CA 92834 USA

With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of nonionic surfactants（ a total of 65 surfactants）. The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0. 962.

关键词： Nonionic surfactants Cloud point quantitative structure-property relationship

投影寻踪回归方法对氢键酸度的定量预测(英文)

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投影寻踪回归方法对氢键酸度的定量预测(英文)