  The inverse problem of designing component interactions to target emergent
structure is fundamental to numerous applications in biotechnology, materials
science, and statistical physics. Equally important is the inverse problem of
designing emergent kinetics, but this has received considerably less attention.
Using recent advances in automatic differentiation, we show how kinetic
pathways can be precisely designed by directly differentiating through
statistical-physics models, namely free energy calculations and molecular
dynamics simulations. We consider two systems that are crucial to our
understanding of structural self-assembly: bulk crystallization and small
nanoclusters. In each case we are able to assemble precise dynamical features.
Using gradient information, we manipulate interactions among constituent
particles to tune the rate at which these systems yield specific structures of
interest. Moreover, we use this approach to learn non-trivial features about
the high-dimensional design space, allowing us to accurately predict when
multiple kinetic features can be simultaneously and independently controlled.
These results provide a concrete and generalizable foundation for studying
non-structural self-assembly, including kinetic properties as well as other
complex emergent properties, in a vast array of systems.
