PEOPLE
- Principal Investigator
JOURNAL
Matter by Cell Press
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ABSTRACT
Progress and potential
Blending polymers is a cost-effective strategy to develop functional materials using existing components, yet the design space is vast, and traditional trial-and-error approaches are inefficient. In this work, we introduce an autonomous, data-driven workflow integrated with a robotic platform for discovering functional random heteropolymer blends. This system successfully identified blends that outperform their individual components in protein stabilization. While previous efforts have focused primarily on the monomer composition of random heteropolymers, our results highlight the potential to make discoveries from complex polymer blend systems. This methodology could be generalized to other material discovery campaigns, from optimizing electrolytes for batteries to improving drug excipient combinations. The dataset released with this study also provides a valuable resource for advancing polymer informatics in blend design.
Highlights
• A data-driven robotic platform was developed to discover functional polymer blends • The platform enabled efficient optimization from high-dimensional blending spaces • Blends of random heteropolymers can outperform individual components in function • Segment-level features correlated with improved protein stabilization
Contributors: Guangqi Wu, Tianyi Jin, Alfredo Alexander-Katz, Connor Coley
Blending polymers is a cost-effective strategy to develop functional materials using existing components, yet the design space is vast, and traditional trial-and-error approaches are inefficient. In this work, we introduce an autonomous, data-driven workflow integrated with a robotic platform for discovering functional random heteropolymer blends. This system successfully identified blends that outperform their individual components in protein stabilization. While previous efforts have focused primarily on the monomer composition of random heteropolymers, our results highlight the potential to make discoveries from complex polymer blend systems. This methodology could be generalized to other material discovery campaigns, from optimizing electrolytes for batteries to improving drug excipient combinations. The dataset released with this study also provides a valuable resource for advancing polymer informatics in blend design.
Highlights
• A data-driven robotic platform was developed to discover functional polymer blends • The platform enabled efficient optimization from high-dimensional blending spaces • Blends of random heteropolymers can outperform individual components in function • Segment-level features correlated with improved protein stabilization
Contributors: Guangqi Wu, Tianyi Jin, Alfredo Alexander-Katz, Connor Coley
