Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature-Reference-Cited by-同舟云学术

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature

Published:2021-10-18 Issue:11 Volume:61 Page:5395-5413
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Tao Lei¹,Varshney Vikas²^ORCID,Li Ying¹³^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States

2. Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright Patterson Air Force Base, Ohio 45433, United States

3. Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, United States

Funder

Air Force Office of Scientific Research

Division of Civil, Mechanical and Manufacturing Innovation

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c01031

Reference117 articles.

1. Polymer informatics: Current status and critical next steps

2. Polymer Informatics: Opportunities and Challenges

3. Machine learning in polymer informatics

4. Wu, S.; Yamada, H.; Hayashi, Y.; Zamengo, M.; Yoshida, R., Potentials and challenges of polymer informatics: exploiting machine learning for polymer design. arXiv preprint 2020; arXiv:2010.07683.

5. Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

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