Learning to learn: Non-equilibrium design protocols for adaptable materials
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.
Cite
@article{arxiv.2211.02270,
title = {Learning to learn: Non-equilibrium design protocols for adaptable materials},
author = {Martin J. Falk and Jiayi Wu and Ayanna Matthews and Vedant Sachdeva and Nidhi Pashine and Margaret Gardel and Sidney Nagel and Arvind Murugan},
journal= {arXiv preprint arXiv:2211.02270},
year = {2022}
}