Work

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

Year: 2026

Type: article

Abstract: 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 multicomponent 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.

Source: Advanced Intelligent Discovery

DOI: 10.1002/aidi.202500177

Authors: Guoyue Xu, Renzheng Zhang, Tengfei Luo

Institution: University of Notre Dame

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, U.S. Department of Energy, Office of Science

Awards: UNDERSTANDING THE STRUCTURE-FUNCTION RELATIONSHIP OF TERNARY SOLUBILITIES IN IONIC LIQUID-WATER-ION SOLUTIONS, Developing and Understanding Thermally Conductive Polymers by Combining Molecular Simulation, Machine Learning and Experiment