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 …
Data-driven design of polymer-based biomaterials: high-throughput simulation, experimentation, and machine learning
Polymers, with the capacity to tunably alter properties and response based on manipulation
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …
[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 …
[HTML][HTML] A graph representation of molecular ensembles for polymer property prediction
Synthetic polymers are versatile and widely used materials. Similar to small organic
molecules, a large chemical space of such materials is hypothetically accessible …
molecules, a large chemical space of such materials is hypothetically accessible …
Featurization strategies for polymer sequence or composition design by machine learning
The emergence of data-intensive scientific discovery and machine learning has dramatically
changed the way in which scientists and engineers approach materials design …
changed the way in which scientists and engineers approach materials design …
Predicting phase behavior of linear polymers in solution using machine learning
JG Ethier, RK Casukhela, JJ Latimer… - …, 2022 - ACS Publications
The phase behavior of polymers in solution is crucial to many applications in polymer
processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer …
processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer …
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 …
[HTML][HTML] 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 …
Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks
IV Volgin, PA Batyr, AV Matseevich, AY Dobrovskiy… - ACS …, 2022 - ACS Publications
In the present work, we address the problem of utilizing machine learning (ML) methods to
predict the thermal properties of polymers by establishing “structure–property” relationships …
predict the thermal properties of polymers by establishing “structure–property” relationships …
Machine learning prediction on the fractional free volume of polymer membranes
Fractional free volume (FFV) characterizes the microstructural level features of polymers and
affects their properties including thermal, mechanical, and separation performance …
affects their properties including thermal, mechanical, and separation performance …