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 …
Emerging trends in machine learning: a polymer perspective
In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …
intelligence as applied to polymer science. Here, we highlight the unique challenges …
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 …
Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular …
Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
Chemistry-informed machine learning for polymer electrolyte discovery
Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by
enhancing safety and enabling higher energy densities. However, SPEs suffer from …
enhancing safety and enabling higher energy densities. However, SPEs suffer from …
Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language
Advances in machine learning (ML) and automated experimentation are poised to vastly
accelerate research in polymer science. Data representation is a critical aspect for enabling …
accelerate research in polymer science. Data representation is a critical aspect for enabling …
Polymer graph neural networks for multitask property learning
O Queen, GA McCarver, S Thatigotla… - npj Computational …, 2023 - nature.com
The prediction of a variety of polymer properties from their monomer composition has been a
challenge for material informatics, and their development can lead to a more effective …
challenge for material informatics, and their development can lead to a more effective …
Self-driving laboratories: A paradigm shift in nanomedicine development
Nanomedicines have transformed promising therapeutic agents into clinically approved
medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA …
medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA …
The rise of machine learning in polymer discovery
In the recent decades, with rapid development in computing power and algorithms, machine
learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the …
learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the …