Machine learning for polymeric materials: an introduction

Morgan M. Cencer, Jeffrey S. Moore, Rajeev S. Assary

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)537-542
Number of pages6
JournalPolymer International
Issue number5
StatePublished - May 2022


  • informatics
  • inverse design
  • machine learning
  • polymers

ASJC Scopus subject areas

  • Materials Chemistry
  • Polymers and Plastics
  • Organic Chemistry


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