Issue 43, 2022

Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning

Abstract

We present machine learning models trained on experimental data to predict room-temperature solubility for any polymer–solvent pair. The new models are a significant advancement over past data-driven work, in terms of protocol, validity, and versatility. A generalizable fingerprinting method is used for the polymers and solvents, making it possible, in principle, to handle any polymer–solvent combination. Our data-driven approach achieves high accuracy when either both the polymer and solvent or just the polymer has been seen during the training phase. Model performance is modest though when a solvent (in a newly queried polymer–solvent pair) is not part of the training set. This is likely because the number of unique solvents in our data set is small (much smaller than the number of polymers). Nevertheless, as the data set increases in size, especially as the solvent set becomes more diverse, the overall predictive performance is expected to improve.

Graphical abstract: Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning

Supplementary files

Article information

Article type
Paper
Submitted
13 aug 2022
Accepted
22 okt 2022
First published
31 okt 2022

Phys. Chem. Chem. Phys., 2022,24, 26547-26555

Author version available

Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning

J. Kern, S. Venkatram, M. Banerjee, B. Brettmann and R. Ramprasad, Phys. Chem. Chem. Phys., 2022, 24, 26547 DOI: 10.1039/D2CP03735A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements