# Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

arXiv: https://arxiv.org/abs/2509.05351 · HTML: https://mlsanity.com/paper/2509.05351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning](https://mlsanity.com/paper/2111.14786/) (2021)
- [AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories](https://mlsanity.com/paper/2405.13930/) (2024)

## Abstract (arXiv)

To overcome the inherent inefficiencies of traditional trial-and-error materials discovery, the scientific community is increasingly developing autonomous laboratories that integrate data-driven decision-making into closed-loop experimental workflows. In this work, we realize this concept for thermoresponsive polymers by developing a low-cost, "frugal twin" platform for the optimization of the lower critical solution temperature (LCST) of poly(N-isopropylacrylamide) (PNIPAM). Our system integrates robotic fluid-handling, on-line sensors, and Bayesian optimization (BO) that navigates the multi-component salt solution spaces to achieve user-specified LCST targets. The platform demonstrates convergence to target properties within a minimal number of experiments. It strategically explores the parameter space, learns from informative "off-target" results, and self-corrects to achieve the final targets. By providing an accessible and adaptable blueprint, this work lowers the barrier to entry for autonomous experimentation and accelerates the design and discovery of functional polymers.