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 …

Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

J Yang, L Tao, J He, JR McCutcheon, Y Li - Science Advances, 2022 - science.org
Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

[HTML][HTML] Integration of machine learning and coarse-grained molecular simulations for polymer materials: physical understandings and molecular design

D Nguyen, L Tao, Y Li - Frontiers in Chemistry, 2022 - frontiersin.org
In recent years, the synthesis of monomer sequence-defined polymers has expanded into
broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing …

Benchmarking machine learning models for polymer informatics: an example of glass transition temperature

L Tao, V Varshney, Y Li - Journal of Chemical Information and …, 2021 - ACS Publications
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 …

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 …

TransPolymer: a Transformer-based language model for polymer property predictions

C Xu, Y Wang, A Barati Farimani - npj Computational Materials, 2023 - nature.com
Accurate and efficient prediction of polymer properties is of great significance in polymer
design. Conventionally, expensive and time-consuming experiments or simulations are …

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 …

Prediction and interpretation of polymer properties using the graph convolutional network

J Park, Y Shim, F Lee, A Rammohan, S Goyal… - ACS Polymers …, 2022 - ACS Publications
We present machine learning models for the prediction of thermal and mechanical
properties of polymers based on the graph convolutional network (GCN). GCN-based …

Polymer informatics at scale with multitask graph neural networks

R Gurnani, C Kuenneth, A Toland… - Chemistry of …, 2023 - ACS Publications
Artificial intelligence-based methods are becoming increasingly effective at screening
libraries of polymers down to a selection that is manageable for experimental inquiry. The …

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

L Tao, J He, NE Munyaneza, V Varshney… - Chemical Engineering …, 2023 - Elsevier
Polyimides have been widely used in modern industries because of their excellent
mechanical and thermal properties, eg, high-temperature fuel cells, displays, and aerospace …