Graph convolutional neural networks for polymers property prediction
A fast and accurate predictive tool for polymer properties is demanding and will pave the
way to iterative inverse design. In this work, we apply graph convolutional neural networks …
way to iterative inverse design. In this work, we apply graph convolutional neural networks …
[HTML][HTML] polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
C Kuenneth, R Ramprasad - Nature Communications, 2023 - nature.com
Polymers are a vital part of everyday life. Their chemical universe is so large that it presents
unprecedented opportunities as well as significant challenges to identify suitable application …
unprecedented opportunities as well as significant challenges to identify suitable application …
Dielectric polymer property prediction using recurrent neural networks with optimizations
Despite the growing success of machine learning for predicting structure–property
relationships in molecules and materials, such as predicting the dielectric properties of …
relationships in molecules and materials, such as predicting the dielectric properties of …
Prediction and interpretation of polymer properties using the graph convolutional network
We present machine learning models for the prediction of thermal and mechanical
properties of polymers based on the graph convolutional network (GCN). GCN-based …
properties of polymers based on the graph convolutional network (GCN). GCN-based …
[HTML][HTML] Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
The efficient and economical exploitation of polymers with high thermal conductivity (TC) is
essential to solve the issue of heat dissipation in organic devices. Currently, the …
essential to solve the issue of heat dissipation in organic devices. Currently, the …
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 …
[图书][B] Polymer science and engineering: the shifting research frontiers
National Research Council, Division on Engineering… - 1994 - books.google.com
Polymers are used in everything from nylon stockings to commercial aircraft to artificial heart
valves, and they have a key role in addressing international competitiveness and other …
valves, and they have a key role in addressing international competitiveness and other …
Prediction of polymer properties using infinite chain descriptors (ICD) and machine learning: Toward optimized dielectric polymeric materials
To facilitate the development of new polymeric materials, we report the development of new
heuristic models to predict the dielectric constant, band gap, dielectric loss tangent, and …
heuristic models to predict the dielectric constant, band gap, dielectric loss tangent, and …
[HTML][HTML] Integration of machine learning and coarse-grained molecular simulations for polymer materials: physical understandings and molecular design
In recent years, the synthesis of monomer sequence-defined polymers has expanded into
broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing …
broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing …