Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

2021 ◽  
Vol 12 (6) ◽  
pp. 843-851 ◽  
Author(s):  
Yun Zhang ◽  
Xiaojie Xu
Keyword(s):  
Machine Learning ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Quantum Chemical ◽  
Quantum Chemical Descriptors ◽  
Model Based ◽  
Chemical Descriptors ◽  
High Degree

Polyacrylamides glass transition temperature predictions from different models, where the GPR model is from the current study. The GPR model based on quantum chemical descriptors shows a high degree of accuracy.

Synthesis of Novel Hyperbranched Polyurethane Acrylate Based on Pentaerythritol

2015 ◽  
Vol 731 ◽  
pp. 515-519
Author(s):  
Yu Xiu Wang ◽  
Guang Xue Chen
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Single Step ◽  
Urethane Acrylate ◽  
Uv Curable ◽  
Hyperbranched Polyesters ◽  
Three Generations ◽  
Two Stages ◽  
High Degree

To study the structure and properties of hyperbranched polyesters as well as the modified ones. Three generations of hyperbranched polyesters named HBPE-1, HBPE-2 and HBPE-3 were synthesized by the reactions from pentaerythritol and 2,2-dihydroxy-methyl-propionic acid by single-step polycondensation. They were characterized by 1H NMR, 13C NMR and the results indicated they possessed a high degree of branching. Then homemade urethane acrylate prepolymer reacted with three generations of hydroxyl-terminated hyperbranched polyesters introducing C=C contents, the product was characterized by FT-IR, DSC, TGA techniques. The results of TGA showed well thermal stability of all the products. TGA curves of the modified HBPES showed two stages, 280-350°C means pyrolysis of main chain ester section and 400-450°C means pyrolysis of urethane acrylate section. The results of DSC indicated that glass transition temperature increased with the number of the hyperbranched polyester units’ increment. What’s more, glass transition temperature of HBPE-3 was 48.13°C. HPUA-3 was semi-crystalline material, its glass transition temperature was-12.59°C, cold crystallization temperature Tc 110.92°C, melting temperature Tm 134.74°C. Since the introduction of a large number of unsaturated units to the end, the resin can be introduced into the UV-curable systems for paints, inks, adhesives and some other fields.

scholarly journals A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

Materials ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 5701
Author(s):  
Zhuoying Jiang ◽  
Jiajie Hu ◽  
Babetta L. Marrone ◽  
Ghanshyam Pilania ◽  
Xiong (Bill) Yu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Deep Neural Network ◽  
Learning Model ◽  
Support Vector ◽  
Model Framework ◽  
Machine Learning Model

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

Prediction of Glass Transition Temperature of Polymer by Support Vector Regression

2012 ◽  
Vol 455-456 ◽  
pp. 436-442
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
B. Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Support Vector Regression ◽  
Density Functional ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Leave One Out

. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

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