Literature DB >> 29201535

Polymer Informatics: Opportunities and Challenges.

Debra J Audus1, Juan J de Pablo2,3.   

Abstract

We are entering an era where large volumes of scientific data, coupled with algorithmic and computational advances, can reduce both the time and cost of developing new materials. This emerging field known as materials informatics has gained acceptance for a number of classes of materials, including metals and oxides. In the particular case of polymer science, however, there are important challenges that must be addressed before one can start to deploy advanced machine learning approaches for designing new materials. These challenges are primarily related to the manner in which polymeric systems and their properties are reported. In this viewpoint, we discuss the opportunities and challenges for making materials informatics as applied to polymers, or equivalently polymer informatics, a reality.

Entities:  

Year:  2017        PMID: 29201535      PMCID: PMC5702941          DOI: 10.1021/acsmacrolett.7b00228

Source DB:  PubMed          Journal:  ACS Macro Lett            Impact factor:   6.903


  10 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  Quantitative structure-property relationship modeling of diverse materials properties.

Authors:  Tu Le; V Chandana Epa; Frank R Burden; David A Winkler
Journal:  Chem Rev       Date:  2012-01-17       Impact factor: 60.622

3.  Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design.

Authors:  Fiorella Cravero; María Jimena Martínez; Gustavo Esteban Vazquez; Mónica Fátima Díaz; Ignacio Ponzoni
Journal:  J Integr Bioinform       Date:  2016-11-27

4.  Can artificial intelligence create the next wonder material?

Authors:  Nicola Nosengo; Gerbrand Ceder
Journal:  Nature       Date:  2016-05-05       Impact factor: 49.962

5.  ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature.

Authors:  Matthew C Swain; Jacqueline M Cole
Journal:  J Chem Inf Model       Date:  2016-10-06       Impact factor: 4.956

6.  Blending Education and Polymer Science: Semi Automated Creation of a Thermodynamic Property Database.

Authors:  Roselyne B Tchoua; Jian Qin; Debra J Audus; Kyle Chard; Ian T Foster; Juan de Pablo
Journal:  J Chem Educ       Date:  2016-08-15       Impact factor: 2.979

7.  A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature.

Authors:  Roselyne B Tchoua; Kyle Chard; Debra Audus; Jian Qin; Juan de Pablo; Ian Foster
Journal:  Procedia Comput Sci       Date:  2016-06-01

8.  Aperiodic Copolymers.

Authors:  Jean-François Lutz
Journal:  ACS Macro Lett       Date:  2014-09-26       Impact factor: 6.903

9.  Machine Learning Strategy for Accelerated Design of Polymer Dielectrics.

Authors:  Arun Mannodi-Kanakkithodi; Ghanshyam Pilania; Tran Doan Huan; Turab Lookman; Rampi Ramprasad
Journal:  Sci Rep       Date:  2016-02-15       Impact factor: 4.379

10.  PubChem Substance and Compound databases.

Authors:  Sunghwan Kim; Paul A Thiessen; Evan E Bolton; Jie Chen; Gang Fu; Asta Gindulyte; Lianyi Han; Jane He; Siqian He; Benjamin A Shoemaker; Jiyao Wang; Bo Yu; Jian Zhang; Stephen H Bryant
Journal:  Nucleic Acids Res       Date:  2015-09-22       Impact factor: 16.971

  10 in total
  15 in total

1.  Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids.

Authors:  Matthew J Tamasi; Roshan A Patel; Carlos H Borca; Shashank Kosuri; Heloise Mugnier; Rahul Upadhya; N Sanjeeva Murthy; Michael A Webb; Adam J Gormley
Journal:  Adv Mater       Date:  2022-06-11       Impact factor: 32.086

2.  Machine learning strategies for the structure-property relationship of copolymers.

Authors:  Lei Tao; John Byrnes; Vikas Varshney; Ying Li
Journal:  iScience       Date:  2022-06-10

3.  Machine learning enables interpretable discovery of innovative polymers for gas separation membranes.

Authors:  Jason Yang; Lei Tao; Jinlong He; Jeffrey R McCutcheon; Ying Li
Journal:  Sci Adv       Date:  2022-07-20       Impact factor: 14.957

4.  Targeted sequence design within the coarse-grained polymer genome.

Authors:  Michael A Webb; Nicholas E Jackson; Phwey S Gil; Juan J de Pablo
Journal:  Sci Adv       Date:  2020-10-21       Impact factor: 14.136

5.  BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules.

Authors:  Tzyy-Shyang Lin; Connor W Coley; Hidenobu Mochigase; Haley K Beech; Wencong Wang; Zi Wang; Eliot Woods; Stephen L Craig; Jeremiah A Johnson; Julia A Kalow; Klavs F Jensen; Bradley D Olsen
Journal:  ACS Cent Sci       Date:  2019-09-12       Impact factor: 14.553

6.  Designing exceptional gas-separation polymer membranes using machine learning.

Authors:  J Wesley Barnett; Connor R Bilchak; Yiwen Wang; Brian C Benicewicz; Laura A Murdock; Tristan Bereau; Sanat K Kumar
Journal:  Sci Adv       Date:  2020-05-15       Impact factor: 14.136

Review 7.  Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.

Authors:  Guang Chen; Zhiqiang Shen; Akshay Iyer; Umar Farooq Ghumman; Shan Tang; Jinbo Bi; Wei Chen; Ying Li
Journal:  Polymers (Basel)       Date:  2020-01-08       Impact factor: 4.329

8.  Higher-order structure of polymer melt described by persistent homology.

Authors:  Yohei Shimizu; Takanori Kurokawa; Hirokazu Arai; Hitoshi Washizu
Journal:  Sci Rep       Date:  2021-01-26       Impact factor: 4.379

9.  ChemProps: A RESTful API enabled database for composite polymer name standardization.

Authors:  Bingyin Hu; Anqi Lin; L Catherine Brinson
Journal:  J Cheminform       Date:  2021-03-12       Impact factor: 5.514

Review 10.  Automation and data-driven design of polymer therapeutics.

Authors:  Rahul Upadhya; Shashank Kosuri; Matthew Tamasi; Travis A Meyer; Supriya Atta; Michael A Webb; Adam J Gormley
Journal:  Adv Drug Deliv Rev       Date:  2020-11-24       Impact factor: 15.470

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