{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-enables-polymer-cloud-point","title":"Machine learning enables polymer cloud-point engineering via inverse design","arxiv_id":"1812.11212","date":"2018-11-21","proceeding":null,"authors":["Kumar Jatin N.","Li Qianxiao","Tang Karen Y. T.","Buonassisi Tonio","Gonzalez-Oyarce Anibal L.","Ye Jun"],"abstract":"Inverse design is an outstanding challenge in disordered systems with\nmultiple length scales such as polymers, particularly when designing polymers\nwith desired phase behavior. We demonstrate high-accuracy tuning of\npoly(2-oxazoline) cloud point via machine learning. With a design space of four\nrepeating units and a range of molecular masses, we achieve an accuracy of 4\n{\\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\\deg}C,\nemploying gradient boosting with decision trees. The RMSE is >3x better than\nlinear and polynomial regression. We perform inverse design via particle-swarm\noptimization, predicting and synthesizing 17 polymers with constrained design\nat 4 target cloud points from 37 to 80 {\\deg}C. Our approach challenges the\nstatus quo in polymer design with a machine learning algorithm, that is capable\nof fast and systematic discovery of new polymers.","url_abs":"http://arxiv.org/abs/1812.11212v1","url_pdf":"http://arxiv.org/pdf/1812.11212v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-enables-polymer-cloud-point","repo_url":"https://github.com/LiQianxiao/CloudPoint-MachineLearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}