Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation
Yang Liu1,2, Xubo Yue3, Junru Zhang1
1Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
ACS Applied Materials & Interfaces
|December 11, 2024
Summary
Collaborative learning using consensus Bayesian optimization accelerates materials discovery. This approach enhances autonomous experimentation (AE) by enabling multiple stakeholders to efficiently discover optimized composite polysaccharide hydrogels.
Area of Science:
- Materials Science
- Chemical Engineering
- Computational Science
Background:
- Standalone self-driving workflows can discover and engineer materials.
- Coordinating multiple stakeholders and workflows is key to advancing autonomous experimentation (AE) for accelerated materials discovery (AMD).
Purpose of the Study:
- To describe a scalable AMD paradigm based on AE and "collaborative learning".
Main Methods:
- A novel consensus Bayesian optimization (BO) model was developed for collaborative learning.
- The collaborative workflow was compared against a non-collaborating AMD workflow.
Main Results:
- The consensus BO model enabled the rapid discovery of mechanically optimized composite polysaccharide hydrogels.
- The collaborative workflow outperformed the non-collaborating workflow over eight experimental iterations.
- Four collaborating clients achieved notable material performance after five iterations.
Conclusions:
- Collaborative learning by consensus BO can enable scaling and performance optimization for self-driving materials research.
- This approach facilitates cooperation between humans and machines for shared material design objectives.
Keywords:
Bayesian optimizationactive learningautonomous experimentationglycomaterialsmaterials genome initiativeMore Related Videos
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