Automated Polymer Synthesis and Data-driven Design

We automate synthesis, purification, analysis, and functional evaluation at small scale to build an experimental platform for comparing diverse polymers under consistent conditions.

Automated Polymer Synthesis and Data-driven Design

We connect human expertise, automated experimentation, and data science to efficiently explore complex polymer structure–function relationships.

Research focus

01

Polymer library synthesis

Diverse candidate polymers with systematically varied compositions, molecular weights, and functional-group combinations are synthesized automatically and in parallel.

02

High-throughput evaluation

Biological activity, toxicity, and interactions with cell membranes are measured and quantified in parallel across many samples to accumulate comparable data.

03

Machine-learning-guided exploration

Bayesian optimization and machine learning propose the next experimental conditions, enabling a closed loop of design, synthesis, and evaluation.

Methods supporting our research

Connect making, measuring, and designing to accelerate biofunctional polymer discovery and drive a paradigm shift.

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