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] 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 …
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 …
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 …
Machine learning for polymeric materials: an introduction
Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …
researchers are using data science and polymer informatics to design new materials and …
[PDF][PDF] Machine learning glass transition temperature of polymers
Y Zhang, X Xu - Heliyon, 2020 - cell.com
As an important thermophysical property, polymers' glass transition temperature, Tg, could
sometimes be difficult to determine experimentally. Modeling methods, particularly data …
sometimes be difficult to determine experimentally. Modeling methods, particularly data …