  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.
