Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning

Authors:

Yang Liu, Xubo Yue, Junru Zhang, Zhenghao Zhai, Ali Moammeri, Kevin J. Edgar, Albert S. Berahas, Raed Al Kontar, Blake N. Johnson

Affiliation:

Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, United States; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States; Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States; Department of Materials Science and Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States; Macromolecules Innovation Institute, Virginia Tech, Blacksburg, Virginia 24061, United States; Department of Sustainable Biomaterials, Virginia Tech, Blacksburg, Virginia 24061, United States

Project Image

Description:

While some materials can be discovered and engineered using standalone self‑driving workflows, coordinating multiple stakeholders and workflows toward a common goal could advance autonomous experimentation (AE) for accelerated materials discovery (AMD). Here, we describe a scalable AMD paradigm based on AE and “collaborative learning”. Collaborative learning using a novel consensus Bayesian optimization (BO) model enabled the rapid discovery of mechanically optimized composite polysaccharide hydrogels. The collaborative workflow outperformed a non‑collaborating AMD workflow scaled by independent learning based on the trend of mechanical property evolution over eight experimental iterations, corresponding to a budget limit. After five iterations, four collaborating clients obtained notable material performance (i.e., composition discovery). Collaborative learning by consensus BO can enable scaling and performance optimization for a range of self‑driving materials research workflows driven by optimally cooperating humans and machines that share a material design objective.

Publications:

Tags:

Carbohydrates High-throughput characterization Hydrogels Machine learning

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