Learning to learn by using nonequilibrium training protocols for adaptable materials

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 120; no. 27; p. e2219558120
• Main Authors: Falk, Martin J, Wu, Jiayi, Matthews, Ayanna, Sachdeva, Vedant, Pashine, Nidhi, Gardel, Margaret L, Nagel, Sidney R, Murugan, Arvind
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 04.07.2023; Proceedings of the National Academy of Sciences
• Subjects: Adaptability; Algorithms; Design; Design parameters; Elastic deformation; Folding; Inverse problems; Learning; Nucleation; Physical Sciences; materials training; adaptability; disordered materials; inverse design
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2219558120

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 such adaptability.