Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break

F Cravero, MF Díaz, I Ponzoni - The Journal of Chemical Physics, 2022 - pubs.aip.org
The artificial intelligence-based prediction of the mechanical properties derived from the
tensile test plays a key role in assessing the application profile of new polymeric materials …

Molecular simulation of thermoplastic polyurethanes under large tensile deformation

S Zhu, N Lempesis, PJ in 't Veld, GC Rutledge - Macromolecules, 2018 - ACS Publications
Thermoplastic polyurethanes (TPUs) are useful materials for numerous applications due in
part to their outstanding resilience and ability to dissipate energy under large mechanical …

A quantitative relationship between Tgs and chain segment structures of polystyrenes

X Yu, X Huang - Polímeros, 2017 - SciELO Brasil
The glass transition temperature (T g) is a fundamental characteristic of an amorphous
polymer. A quantitative structure-property relationship (QSPR) based on error back …

Thermal analysis of the structure of segmented polyurethane elastomers: relation to mechanical properties

K Bagdi, K Molnár, B Pukánszky… - Journal of thermal analysis …, 2009 - Springer
Abstract Polyurethanes were prepared from 4, 4′-methylenebis (phenyl isocyanate)(MDI),
1, 4-butanediol (BD), and poly (tetrahydrofurane) polyether polyol (PTHF) by melt …

Linking the morphology of a high hard segment content polyurethane to its thermal behaviour and mechanical properties

Y Swolfs, E Bertels, I Verpoest, B Goderis - Polymer, 2015 - Elsevier
Understanding and controlling the morphology of thermoplastic polyurethane (TPU) is
crucial, as it is closely linked with its thermal and mechanical properties. The morphology of …

Polymer genome: a data-powered polymer informatics platform for property predictions

C Kim, A Chandrasekaran, TD Huan… - The Journal of …, 2018 - ACS Publications
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 …

Designing temperature-memory effects in semicrystalline polyurethane

N Mirtschin, T Pretsch - Rsc Advances, 2015 - pubs.rsc.org
Temperature-memory polymers are able to generate a substantial mechanical response
when heated above the temperature, at which a preceding deformation was carried out …

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular …

A Boublia, T Lemaoui, J AlYammahi… - ACS Sustainable …, 2022 - ACS Publications
Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …

Modeling the temperature dependence of dynamic mechanical properties and visco-elastic behavior of thermoplastic polyurethane using artificial neural network

I Kopal, M Harničárová, J Valíček, M Kušnerová - Polymers, 2017 - mdpi.com
This paper presents one of the soft computing methods, specifically the artificial neural
network technique, that has been used to model the temperature dependence of dynamic …

Predicting phase behavior of linear polymers in solution using machine learning

JG Ethier, RK Casukhela, JJ Latimer… - …, 2022 - ACS Publications
The phase behavior of polymers in solution is crucial to many applications in polymer
processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer …