Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning
Nature has only provided us with a limited number of biobased and biodegradable building
blocks. Therefore, the fine-tuning of the sustainable polymer properties is expected to be …
blocks. Therefore, the fine-tuning of the sustainable polymer properties is expected to be …
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 …
Prediction of the glass transition temperature and design of phase diagrams of butadiene rubber and styrene–butadiene rubber via molecular dynamics simulations
MS Ryu, HG Kim, HY Kim, KS Min, HJ Kim… - Physical Chemistry …, 2017 - pubs.rsc.org
To prevent car accidents, it is important to evaluate the thermal stability of tire rubbers, such
as natural rubber (NR), butadiene rubber (BR), and styrene–butadiene rubber (SBR) …
as natural rubber (NR), butadiene rubber (BR), and styrene–butadiene rubber (SBR) …
Predicting crystallization tendency of polymers using multifidelity information fusion and machine learning
The degree of crystallinity of a polymer is a critical parameter that controls a variety of
polymer properties. A high degree of crystallinity is associated with excellent mechanical …
polymer properties. A high degree of crystallinity is associated with excellent mechanical …
Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures
D Palomba, GE Vazquez, MF Díaz - Journal of Molecular Graphics and …, 2012 - Elsevier
New descriptors of main and side chains for polymers with high molecular weight are
presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio …
presented in order to predict the glass-transition temperature (Tg) by means of Tg/M ratio …
Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization
The molar mass of the polyurethanes (PUs)'reagents directly influences their thermal
response, affecting both the polymerization process and the enthalpy and the degree of …
response, affecting both the polymerization process and the enthalpy and the degree of …
Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation
To enable the design and development of the next generation of high-performance
composite materials, there is a need to establish improved computational simulation …
composite materials, there is a need to establish improved computational simulation …
Computational neural networks and the rational design of polymeric materials: the next generation polycarbonates
CW Ulmer II, DA Smith, BG Sumpter, DI Noid - … and theoretical polymer …, 1998 - Elsevier
We present an atomistic approach to computer aided molecular design that incorporates
computational neural networks as the tools for determining accurate structure–property …
computational neural networks as the tools for determining accurate structure–property …
Facilitating polymer property prediction with machine learning and group interaction modelling methods
Identification of a suitable polymer material for a given application requires information
about the properties and behavior of the material, which is time-consuming and costly to …
about the properties and behavior of the material, which is time-consuming and costly to …
Atomistic simulation of a thermoplastic polyurethane and micromechanical modeling
Thermoplastic polyurethanes constitute a versatile family of materials with a broad variety of
engineering applications. However, connection between their chemical structure and …
engineering applications. However, connection between their chemical structure and …