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Learning to learn by using nonequilibrium training protocols for adaptable materials
Martin J Falk1, Jiayi Wu1, Ayanna Matthews2
1Department of Physics, The University of Chicago, Chicago, IL 60637.
Summary
Synthetic materials can be trained for adaptability, performing multiple functions by switching targets. This strategy enables materials like elastic networks and heteropolymers to adapt with minimal design changes, revealing physical principles of adaptability.
Area of Science:
- Materials Science
- Computational Science
- Biophysics
Background:
- Biological systems evolve adaptability to thrive in changing environments.
- Environmentally responsive materials offer potential for synthetic adaptability.
- High-dimensional inverse problems in materials design present challenges for achieving multiple functionalities.
Purpose of the Study:
- To develop a training strategy for synthetic materials to achieve adaptability.
- To demonstrate the ability of materials to perform incompatible functionalities with minimal parameter changes.
- To explore the physical principles underlying material adaptability.
Main Methods:
- Utilizing high-dimensional inverse problems for materials design.
- Employing a target-switching algorithm to train material adaptability.
- Simulating adaptable elastic networks and heteropolymer folding pathways.
Main Results:
- Successfully trained materials to switch between incompatible functionalities.
- Achieved functional switching with minimal changes in design parameters.
- Identified physical principles like nucleation-controlled folding enabling adaptability.
Conclusions:
- A novel training strategy enables synthetic materials to exhibit adaptive behaviors.
- Minimal design parameter changes can unlock diverse functionalities in materials.
- The approach provides insights into physical mechanisms governing material adaptability.
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