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نتیجه جستجو - QSPR

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 From chemical structure to quantitative polymer properties prediction through convolutional neural networks
از ساختار شیمیایی گرفته تا پیش بینی کمی از خواص پلیمر از طریق شبکه های عصبی در هم تنیده -2020
In this work convolutional-fully connected neural networks were designed and trained to predict the glass transition temperature of polymers based only on their chemical structure. This approach has shown to successfully predict the Tg of unknown polymers with average relative errors as low as 6%. Several networks with different architecture or hiperparameters were successfully trained using a previously studied glass transition temperatures dataset for validation, and then the same method was employed for an extended dataset, with larger Tg dispersion and polymer’s structure variability. This approach has shown to be accurate and reliable, and does not require any time consuming or expensive measurements and calculations as inputs. Furthermore, it is expected that this method can be easily extended to predict other properties. The possibility of predicting the properties of polymers not even synthesized will save time and resources for industrial development as well as accelerate the scientific understanding of structure-properties relationships in polymer science.
Keywords: QSPR | Properties prediction | Deep learning | Neural network | Smart design
مقاله انگلیسی
2 QSPR study of the Henry’s law constant forheterogeneous compounds
مطالعه QSPR ترکیبات ناهمگن ثابت قانون هنری-2020
We establish a Quantitative Structure-Property Relationships study on the Henry’s lawconstant of 530 heterogeneous compounds, including pesticides, solvents, aromatichydrocarbons and persistent pollutants. The multivariable linear regression models areestablished with the Replacement Method (RM) technique, by searching the best 1–8molecular descriptors on 26,795 available non-conformational structural variables. Thesedescriptors are derived from different freely available softwares, such as PaDEL, Mold2,DataWarrior, QuBiLs-MAS and CORAL. The present results are compared with the estima-tions provided by the HENRYWIN module of EPI Suite, and serve as a tool for predicting theHenry’s law constant on related chemical structures.
Keywords:Henry’s law constant | Quantitative structure-property | relationships | Replacement method | Pesticides | Molecular descriptors
مقاله انگلیسی
3 OCPMDM: Online computation platform for materials data mining
OCPMDM: پلت فرم محاسبات آنلاین برای داده کاوی مواد-2018
With the rapid development of the Materials Genome Initiative (MGI), scientists and engineers are confronted with the need to conduct sophisticated data analytics in modeling the behaviors of materials. Nowadays, it is inconvenient for material researchers to carry out materials data mining work without an efficient platform for materials machine learning. So, it is meaningful to develop an online platform for material researchers in urgent need of using machine learning techniques by themselves. The typical case study is given to demonstrate the applications of the online computation platform for material data mining (OCPMDM) in our lab: The quantitative structure property relationship (QSPR) model for rapid prediction of Curie temperature of perovskite material can be applied to screen out perovskite candidates with higher Curie temperature than those of training dataset collected from references, efficiently. Material data mining tasks can be implemented via the OCPMDM, which provides powerful tools for material researchers in machine learning-assisted materials design and optimization.
Keywords: Machine learning ، Data mining ، Materials design ، MGI ، Web service
مقاله انگلیسی
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بازدید امروز: 9416 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 9416 :::::::: افراد آنلاین: 67