Applied machine learning as a driver for polymeric biomaterials design
SM McDonald, EK Augustine, Q Lanners… - Nature …, 2023 - nature.com
Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
A review on the application of molecular descriptors and machine learning in polymer design
Polymers are an important class of materials with vast arrays of physical and chemical
properties and have been widely used in many applications and industrial products …
properties and have been widely used in many applications and industrial products …
Machine learning enables interpretable discovery of innovative polymers for gas separation membranes
Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …
implications. Despite decades of research, design of new membrane materials remains a …
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 …
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 …
TransPolymer: a Transformer-based language model for polymer property predictions
Accurate and efficient prediction of polymer properties is of great significance in polymer
design. Conventionally, expensive and time-consuming experiments or simulations are …
design. Conventionally, expensive and time-consuming experiments or simulations are …
Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical …
This study employs various machine learning algorithms to model the electrical conductivity
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
Understanding and modeling polymers: The challenge of multiple scales
F Schmid - ACS Polymers Au, 2022 - ACS Publications
Polymer materials are multiscale systems by definition. Already the description of a single
macromolecule involves a multitude of scales, and cooperative processes in polymer …
macromolecule involves a multitude of scales, and cooperative processes in polymer …
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 …
Polymer informatics at scale with multitask graph neural networks
Artificial intelligence-based methods are becoming increasingly effective at screening
libraries of polymers down to a selection that is manageable for experimental inquiry. The …
libraries of polymers down to a selection that is manageable for experimental inquiry. The …