P. Ganga Raju Achary , Sanija Begum , Alla P. Toropova , Andrey A. Toropov
{"title":"基于准SMILES的丙烯聚合Ziegler−Natta催化剂吸附能预测的QSPR方法","authors":"P. Ganga Raju Achary , Sanija Begum , Alla P. Toropova , Andrey A. Toropov","doi":"10.1016/j.md.2016.12.003","DOIUrl":null,"url":null,"abstract":"<div><p>QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.</p></div>","PeriodicalId":100888,"journal":{"name":"Materials Discovery","volume":"5 ","pages":"Pages 22-28"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.md.2016.12.003","citationCount":"9","resultStr":"{\"title\":\"A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization\",\"authors\":\"P. Ganga Raju Achary , Sanija Begum , Alla P. Toropova , Andrey A. Toropov\",\"doi\":\"10.1016/j.md.2016.12.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.</p></div>\",\"PeriodicalId\":100888,\"journal\":{\"name\":\"Materials Discovery\",\"volume\":\"5 \",\"pages\":\"Pages 22-28\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.md.2016.12.003\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352924516300370\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Discovery","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352924516300370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization
QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.