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The inverse problem of designing component interactions to target emergentstructure is fundamental to numerous applications in biotechnology, materialsscience, and statistical physics. Equally important is the inverse problem ofdesigning emergent kinetics, but this has received considerably less attention.Using recent advances in automatic differentiation, we show how kineticpathways can be precisely designed by directly differentiating throughstatistical-physics models, namely free energy calculations and moleculardynamics simulations. We consider two systems that are crucial to ourunderstanding of structural self-assembly: bulk crystallization and smallnanoclusters. In each case we are able to assemble precise dynamical features.Using gradient information, we manipulate interactions among constituentparticles to tune the rate at which these systems yield specific structures ofinterest. Moreover, we use this approach to learn non-trivial features aboutthe high-dimensional design space, allowing us to accurately predict whenmultiple kinetic features can be simultaneously and independently controlled.These results provide a concrete and generalizable foundation for studyingnon-structural self-assembly, including kinetic properties as well as othercomplex emergent properties, in a vast array of systems.

Self-assembling kinetics: Accessing a new design space via differentiable statistical-physics models