Control of Self-Assembly with Dynamic Programming

Martha Grover1, Daniel J Griffin2, Xun Tang1

  • 1Georgia Institute of Technology
  • 2Amgen Inc.



Plenary Session


08:40 - 09:40 | Wed 24 Apr | Catamarã | WePP

Plenary Martha Grover

Full Text


The organization of a large collection of particles into an ordered crystalline array is needed for many applications, including pharmaceutical separations, nuclear waste disposal, and optoelectronic metamaterials. Due to improvements in sensing technology, it is now becoming possible to monitor the crystalline state in real time during the crystallization process, and this sensor technology opens up new possibilities for feedback control. Here we monitor the crystalline state and use this data to build an empirical Markov state model. An optimal feedback policy is then calculated using the empirical model along with dynamic programming. Alternatively, the empirical model can be calculated from simulation "data" coming from a detailed many-body simulation. Experimental results demonstrating the method will be presented for molecular crystallization and colloidal crystallization.

Additional Information

This conference paper updates a previously-reported methodology for establishing feedback control of self-assembly (Griffin et al. (2016b)). The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. The strategy is further demonstrated, with experimental results, for two applications: control of colloidal assembly (to produce perfect colloidal crystals) and control of crystallization from solution (to produce crystals of desired average size).


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