@article{33cb84dec12a428f8ec726c2dc8b12d7,
title = "Machine learning for polymeric materials: an introduction",
abstract = "Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure–property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis.",
keywords = "informatics, inverse design, machine learning, polymers",
author = "Cencer, {Morgan M.} and Moore, {Jeffrey S.} and Assary, {Rajeev S.}",
note = "The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science Laboratory, is operated under Contract no. DE‐AC02‐06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid‐up nonexclusive irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan We acknowledge UChicago/Argonne, CDAC funding via AI for Electrochemistry program. The authors thank Dorothy Loudermilk for assistance in making figures.",
year = "2022",
month = may,
doi = "10.1002/pi.6345",
language = "English (US)",
volume = "71",
pages = "537--542",
journal = "Polymer International",
issn = "0959-8103",
publisher = "John Wiley & Sons, Ltd.",
number = "5",
}