Machine learning enables polymer cloud-point engineering via inverse design

Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four...

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Veröffentlicht in:npj computational materials Jg. 5; H. 1; S. 1 - 6
Hauptverfasser: Kumar, Jatin N., Li, Qianxiao, Tang, Karen Y. T., Buonassisi, Tonio, Gonzalez-Oyarce, Anibal L., Ye, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 12.07.2019
Nature Publishing Group
Schlagworte:
639/301/1034/1037
639/301/923/1028
639/638/455/941
Algorithms
Artificial intelligence
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Decision trees
Design optimization
Inverse design
Learning algorithms
Machine learning
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Polymers
Polynomials
Root-mean-square errors
Theoretical
ISSN:2057-3960, 2057-3960
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Zusammenfassung:Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 °C root mean squared error (RMSE) in a temperature range of 24–90 °C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 °C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.
Bibliographie:ObjectType-Article-1
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ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-019-0209-9