Integration of Liquid Handling Robotics, Turbidimetry, and Machine Learning Toward Inverse Design of Lower Critical Solution Temperature Polymer Formulations

Precise tuning of physical properties, such as lower critical solution temperature (LCST) behavior in polymer formulations, is essential for stimuli‐responsive materials design, but is typically limited by low‐throughput cloud point ( T _cp ) determination. To address this, we developed a high‐throughput (HTP) experimentation platform that integrates automated liquid handling, parallel turbidimetry measurements, and machine learning (ML) under a unified Python framework. Using an OT‐2 liquid handling robot interfaced with a SPECTROstar Nano plate reader, we enabled automated design of experiments‐based sampling of high‐dimensional formulations, which are prepared robotically and analyzed via HTP turbidimetry to determine polymer cloud points ( T _cp ) for P(NIPAM‐co‐DMAEMA) copolymers. Overcoming single‐variable resolution in traditional methods, our approach enables the simultaneous T _cp mapping across polymer, NaCl, and NaOH concentrations, achieving an average measurement throughput of 8 min per sample. This workflow generates high‐fidelity datasets comprising hundreds of formulation –T _cp relationships within days. Supervised ML models trained on these data achieved high predictive accuracy ( R ² up to 0.96; mean squared error as low as 0.023 °C ² ), with Shapley Additive Explanations analyses elucidating feature contributions. T _cp distribution analysis identified polymer concentration as a dominant driver of T _cp variance, particularly in hydrophilic regimes. Using ML, inverse design of formulations was demonstrated, predicting compositions that achieve target transition temperatures with less than 1% error. By combining automated formulation, HTP cloud point measurement, and ML in one workflow, our platform addresses the asymmetry in throughput between polymer synthesis and physical property characterization that currently limits data‐driven LCST design.