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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media
2021
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| Online Access: | https://hdl.handle.net/1721.1/130296 |
| _version_ | 1826212930689957888 |
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| author | Kumar, Jatin N. Li, Qianxiao Tang, Karen Y. T. Buonassisi, Anthony Gonzalez-Oyarce, Anibal L. Ye, Jun |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Kumar, Jatin N. Li, Qianxiao Tang, Karen Y. T. Buonassisi, Anthony Gonzalez-Oyarce, Anibal L. Ye, Jun |
| author_sort | Kumar, Jatin N. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:40:25Z |
| format | Article |
| id | mit-1721.1/130296 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:40:25Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media |
| record_format | dspace |
| spelling | mit-1721.1/1302962022-09-29T15:25:05Z Machine learning enables polymer cloud-point engineering via inverse design Kumar, Jatin N. Li, Qianxiao Tang, Karen Y. T. Buonassisi, Anthony Gonzalez-Oyarce, Anibal L. Ye, Jun Massachusetts Institute of Technology. Department of Mechanical Engineering 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. 2021-03-30T21:02:47Z 2021-03-30T21:02:47Z 2019-07 2019-01 2020-06-24T19:33:04Z Article http://purl.org/eprint/type/JournalArticle 2057-3960 https://hdl.handle.net/1721.1/130296 Kumar, Jatin N. et al. "Machine learning enables polymer cloud-point engineering via inverse design." npj Computational Materials 5, 1 (July 2019): 73. © 2019 The Author(s) en http://dx.doi.org/10.1038/s41524-019-0209-9 npj Computational Materials Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media Nature |
| spellingShingle | Kumar, Jatin N. Li, Qianxiao Tang, Karen Y. T. Buonassisi, Anthony Gonzalez-Oyarce, Anibal L. Ye, Jun Machine learning enables polymer cloud-point engineering via inverse design |
| title | Machine learning enables polymer cloud-point engineering via inverse design |
| title_full | Machine learning enables polymer cloud-point engineering via inverse design |
| title_fullStr | Machine learning enables polymer cloud-point engineering via inverse design |
| title_full_unstemmed | Machine learning enables polymer cloud-point engineering via inverse design |
| title_short | Machine learning enables polymer cloud-point engineering via inverse design |
| title_sort | machine learning enables polymer cloud point engineering via inverse design |
| url | https://hdl.handle.net/1721.1/130296 |
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