  The ability to control forces between sub-micron-scale building blocks offers
considerable potential for designing new materials through self-assembly. A
typical paradigm is to first identify a particular (crystal) structure that has
some desired property, and then design building-block interactions so that this
structure assembles spontaneously. While significant theoretical and
experimental progress has been made in assembling complicated structures in a
variety of systems, this two-step paradigm fundamentally fails for structurally
disordered solids, which lack a well-defined structure to use as a target. Here
we show that disordered solids can still be treated from an inverse
self-assembly perspective by targeting material properties directly. Using the
Poisson's ratio, $\nu$, as a primary example, we show how differentiable
programming connects experimentally relevant interaction parameters with
emergent behavior, allowing us to iteratively "train" the system until we find
the set of interactions that leads to the Poisson's ratio we desire. Beyond the
Poisson's ratio, we also tune the pressure and a measure of local 8-fold
structural order, as well as multiple properties simultaneously, demonstrating
the potential for nontrivial design in disordered solids. This approach is
highly robust, transferable, and scalable, can handle a wide variety of model
systems, properties of interest, and preparation dynamics, and can optimize
over 100s or even 1000s of parameters. This result connects the fields of
disordered solids and inverse self-assembly, indicating that many of the tools
and ideas that have been developed to understand the assembly of crystals can
also be used to control the properties of disordered solids.
