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

## TL;DR

This paper demonstrates how machine learning can accurately predict and inverse-design polymers with specific cloud points, significantly improving over traditional regression methods and enabling rapid discovery of new polymer materials.

## Contribution

The study introduces a machine learning approach using gradient boosting to precisely predict and inverse-design polymer cloud points, outperforming linear models and enabling systematic polymer discovery.

## Key findings

- Achieved 4°C RMSE in cloud point prediction across 24-90°C range.
- Successfully inverse-designed 17 polymers with targeted cloud points.
- Outperformed linear and polynomial regression by over 3 times in accuracy.

## Abstract

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 {\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\deg}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 {\deg}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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Source: https://tomesphere.com/paper/1812.11212