Machine-learning-based predictive modeling of glass transition temperatures: a case of polyhydroxyalkanoate homopolymers and copolymers
Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and
economically viable and possessing a broad range of tunable properties—are currently …
economically viable and possessing a broad range of tunable properties—are currently …
Machine learning for melting temperature predictions and design in polyhydroxyalkanoate-based biopolymers
KK Bejagam, J Lalonde, CN Iverson… - The Journal of …, 2022 - ACS Publications
Diminishing fossil fuel-based resources and ever-growing environmental concerns related
to plastic pollution demand for the development of sustainable and biodegradable polymeric …
to plastic pollution demand for the development of sustainable and biodegradable polymeric …
Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular …
Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
[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 …
[PDF][PDF] Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure
Predicting the glass transition temperature (T g) is of critical importance as it governs the
thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive …
thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive …
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 …
[PDF][PDF] Polymer informatics with multi-task learning
Modern data-driven tools are transforming application-specific polymer development cycles.
Surrogate models that can be trained to predict properties of polymers are becoming …
Surrogate models that can be trained to predict properties of polymers are becoming …
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 …
Polymer genome: a data-powered polymer informatics platform for property predictions
The recent successes of the Materials Genome Initiative have opened up new opportunities
for data-centric informatics approaches in several subfields of materials research, including …
for data-centric informatics approaches in several subfields of materials research, including …