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Issue 1, 2017
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The construction and application of Markov state models for colloidal self-assembly process control

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Abstract

Markov state models have been widely applied to study time sequential events in a variety of disciplines. Due to their versatility for representing system stochasticity, Markov state models hold the promise to simplify simulation and design control policies for colloidal self-assembly systems. In this manuscript, we investigate the effects of state discretization, transition time, sampling approach, and the number of samples on the accuracy of a Markov state model for a colloidal self-assembly process. The model accuracy is evaluated based on the performance of the optimal control policy, calculated with a Markov decision process-based optimization framework, for controlling a Brownian dynamics simulation to produce perfect crystals. The results suggest using a dynamic sampling, a transition time similar to the system characteristic time, a clustering-based state discretization, and an average of at least five samples per state, to efficiently build an accurate Markov state model.

Graphical abstract: The construction and application of Markov state models for colloidal self-assembly process control

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Publication details

The article was received on 31 Oct 2016, accepted on 20 Dec 2016 and first published on 21 Dec 2016


Article type: Paper
DOI: 10.1039/C6ME00092D
Citation: Mol. Syst. Des. Eng., 2017,2, 78-88
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    The construction and application of Markov state models for colloidal self-assembly process control

    X. Tang, M. A. Bevan and M. A. Grover, Mol. Syst. Des. Eng., 2017, 2, 78
    DOI: 10.1039/C6ME00092D

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