Learning to learn: Non-equilibrium design protocols for adaptable materials

TL;DR

This paper introduces a novel learning-based approach to design adaptable materials capable of switching functionalities with minimal modifications, inspired by biological evolution and demonstrated through simulations of elastic networks and heteropolymers.

Contribution

It proposes a periodic target-switching strategy in inverse design algorithms to create materials with multiple functionalities and minimal parameter changes.

Findings

Designs enable functionality switching with minimal parameter adjustments

Simulations demonstrate adaptability in elastic networks and heteropolymers

Reveals physical principles like nucleation-controlled folding for adaptability

Abstract

Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally-responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes; and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable adaptability.