期刊
NPJ COMPUTATIONAL MATERIALS
卷 5, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41524-019-0209-9
关键词
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资金
- AME Programmatic Fund, Agency for Science, Technology, and Research [A1898b0043]
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 degrees C root mean squared error (RMSE) in a temperature range of 24-90 degrees 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 degrees 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.
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