Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature

2021 ◽  
Author(s):  
Lei Tao ◽  
Vikas Varshney ◽  
Ying Li
Keyword(s):  
Machine Learning ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Learning Models ◽  
Machine Learning Models

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.

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.

scholarly journals Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3653
Author(s):  
Franklin Langlang Lee ◽  
Jaehong Park ◽  
Sushmit Goyal ◽  
Yousef Qaroush ◽  
Shihu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Melting Temperature ◽  
Linear Models ◽  
Chemical Properties ◽  
Tensile Modulus ◽  
Rotational Degree ◽  
Structure Property

Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have a large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure–property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (Tg), melting temperature (Tm), density (ρ), and tensile modulus (E). The non-linear model using random forest is in general found to be more accurate than linear regression; however, using feature selection or regularization, the accuracy of linear models is shown to be improved significantly to become comparable to the more complex nonlinear algorithm. We find that none of the models or fingerprints were able to accurately predict the tensile modulus E, which we hypothesize is due to heterogeneity in data and data sources, as well as inherent challenges in measuring it. Finally, QSPR models revealed that the fraction of rotatable bonds, and the rotational degree of freedom affects polyamide properties most profoundly and can be used for back of the envelope calculations for a quick estimate of the polymer attributes (glass transition temperature, melting temperature, and density). These QSPR models, although having slightly lower prediction accuracy, show the most promise for the polymer chemist seeking to develop an intuition of ways to modify the chemistry to enhance specific attributes.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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