Machine Learning in Polymer Research
Abstract: Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Machine Learning in Polymer Research ; day:09 ; month:02 ; year:2025 ; extent:47
Advanced materials ; (09.02.2025) (gesamt 47)
- Urheber
-
Ge, Wei
De Silva, Ramindu
Fan, Yanan
Sisson, Scott A.
Stenzel, Martina H.
- DOI
-
10.1002/adma.202413695
- URN
-
urn:nbn:de:101:1-2502101319072.888124905841
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:20 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ge, Wei
- De Silva, Ramindu
- Fan, Yanan
- Sisson, Scott A.
- Stenzel, Martina H.
Ähnliche Objekte (12)
Machine learning in pain research
The Rise of Machine Learning in Polymer Discovery
Machine learning in polymer additive manufacturing: a review
Machine learning for carbon fiber reinforced polymer production
Machine Learning Applications in Psychotherapy Research
Machine learning and deep learning applications in microbiome research
Application of machine learning in bacteriophage research
Machine Learning in Nutritional Follow-up Research
Machine Learning Pipeline for Predictive Maintenance in Polymer 3D Printing
Machine learning enables polymer cloud-point engineering via inverse design
Explanatory Interactive Machine Learning : Establishing an Action Design Research Process for Machine Learning Projects
Tree-based Machine Learning Methods for Survey Research
Machine learning in pain research
The Rise of Machine Learning in Polymer Discovery
Machine learning in polymer additive manufacturing: a review
Machine learning for carbon fiber reinforced polymer production
Machine Learning Applications in Psychotherapy Research
Machine learning and deep learning applications in microbiome research
Application of machine learning in bacteriophage research
Machine Learning in Nutritional Follow-up Research
Machine Learning Pipeline for Predictive Maintenance in Polymer 3D Printing
Machine learning enables polymer cloud-point engineering via inverse design
Explanatory Interactive Machine Learning : Establishing an Action Design Research Process for Machine Learning Projects
Tree-based Machine Learning Methods for Survey Research
Machine learning in pain research
The Rise of Machine Learning in Polymer Discovery
Machine learning in polymer additive manufacturing: a review
Machine learning for carbon fiber reinforced polymer production
Machine Learning Applications in Psychotherapy Research
Machine learning and deep learning applications in microbiome research
Application of machine learning in bacteriophage research
Machine Learning in Nutritional Follow-up Research
Machine Learning Pipeline for Predictive Maintenance in Polymer 3D Printing
Machine learning enables polymer cloud-point engineering via inverse design
Explanatory Interactive Machine Learning : Establishing an Action Design Research Process for Machine Learning Projects