Benchmarking machine learning models for polymer informatics: an example of glass transition temperature
In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …
glass transition temperature T g and other properties of polymers has attracted extensive …
Predicting polymers' glass transition temperature by a chemical language processing model
We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …
[PDF][PDF] Machine learning discovery of high-temperature polymers
To formulate a machine learning (ML) model to establish the polymer's structure-property
correlation for glass transition temperature T g, we collect a diverse set of nearly 13,000 real …
correlation for glass transition temperature T g, we collect a diverse set of nearly 13,000 real …
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
LA Miccio, GA Schwartz - Polymer, 2020 - Elsevier
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 …
predict the glass transition temperature of polymers based only on their chemical structure …
Copolymer informatics with multitask deep neural networks
Polymer informatics tools have been recently gaining ground to efficiently and effectively
develop, design, and discover new polymers that meet specific application needs. So far …
develop, design, and discover new polymers that meet specific application needs. So far …
Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
Over the past decade, there has been a resurgence in the importance of data-driven
techniques in materials science and engineering. The utilization of state-of-the art …
techniques in materials science and engineering. The utilization of state-of-the art …
Machine learning with enormous “synthetic” data sets: Predicting glass transition temperature of polyimides using graph convolutional neural networks
IV Volgin, PA Batyr, AV Matseevich, AY Dobrovskiy… - ACS …, 2022 - ACS Publications
In the present work, we address the problem of utilizing machine learning (ML) methods to
predict the thermal properties of polymers by establishing “structure–property” relationships …
predict the thermal properties of polymers by establishing “structure–property” relationships …
Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations
Predicting the glass-transition temperatures (T g) of glass-forming polymers is of critical
importance as it governs the thermophysical properties of polymeric materials. The …
importance as it governs the thermophysical properties of polymeric materials. The …
Machine learning in polymer informatics
W Sha, Y Li, S Tang, J Tian, Y Zhao, Y Guo, W Zhang… - InfoMat, 2021 - Wiley Online Library
Polymers have been widely used in energy storage, construction, medicine, aerospace, and
so on. However, the complexity of chemical composition and morphology of polymers has …
so on. However, the complexity of chemical composition and morphology of polymers has …
Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors
Y Zhang, X Xu - Polymer Chemistry, 2021 - pubs.rsc.org
Glass transition temperature, Tg, is an important thermophysical property of
polyacrylamides, which can be difficult to determine experimentally and resource-intensive …
polyacrylamides, which can be difficult to determine experimentally and resource-intensive …