2025-07-28 マサチューセッツ工科大学 (MIT)
＜関連情報＞
- https://news.mit.edu/2025/new-system-dramatically-speeds-polymer-materials-search-0728
- https://www.cell.com/matter/abstract/S2590-2385(25)00379-0
進化型配合最適化による機能性ランダムヘテロポリマーブレンドの自律的発見 Autonomous discovery of functional random heteropolymer blends through evolutionary formulation optimization
Guangqi Wu (吴广启) ∙ Tianyi Jin (金天逸) ∙ Alfredo Alexander-Katz ∙ Connor W. Coley
Matter Published:July 28, 2025
DOI:https://doi.org/10.1016/j.matt.2025.102336
Graphical 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
Summary
While developing new polymers typically requires years of investigation, blending existing polymers offers a cost-effective strategy to create new materials. However, developing functional polymer blends is often a slow and challenging process due to their vast design space, the non-additive nature of polymer properties, and limited fundamental understanding to guide the optimization. Here, we report an autonomous platform that addresses these challenges by integrating high-throughput blending, real-time data acquisition, and an evolutionary algorithm for composition optimization. This approach enables rapid exploration of complex combinatorial blending spaces of random heteropolymers (RHPs). With enzyme thermal stability as a model objective, this system discovered RHP blends (RHPBs) that outperform all constituents. Retrospective analysis reveals segment-level interactions correlated with the performance. This work highlights the opportunity for materials discovery within the RHP and RHPB space and the immense potential of leveraging autonomous discovery platforms to accelerate the discovery of blends with emergent properties.
