# Machine learning enables polymer cloud-point engineering via inverse design

arXiv: https://arxiv.org/abs/1812.11212 · HTML: https://mlsanity.com/paper/1812.11212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation](https://mlsanity.com/paper/2609.16028/) (2026)

## Abstract (arXiv)

Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. 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 degC root mean squared error (RMSE) in a temperature range of 24-90 degC, 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 degC. 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.