Abstract
As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the thermodynamic and kinetics based models used to describe traditional bulk phase active syntheses. Like the statistical mechanics underpinning these latter theories, the framework we propose uses state probabilities as its fundamental objects; but, contrary to the Gibbsian paradigm, our theory directly models the transition probabilities between the initial and final states of a trajectory, foregoing the need to assume ergodicity. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We demonstrate the application of this framework to a simple toy model in which three identical molecules can assemble in one of two ways and conclude with a discussion of how the high computational cost of such a fine-grained model can be overcome through approximation when extending it to larger, more complex systems.
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Acknowledgments
The work described in this document was funded under the U.S. Army Basic Research Program under PE 61102, Project T25, Task 02 “Network Science Initiative” and was managed and executed at the U.S. Army ERDC. Opinions, interpretations, conclusions, and recommendations are those of the author(s) and are not necessarily endorsed by the U.S. Army.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Pilkiewicz, K.R., Rana, P., Mayo, M.L., Ghosh, P. (2019). Self-Assembly from a Single-Molecule Perspective. In: Compagnoni, A., Casey, W., Cai, Y., Mishra, B. (eds) Bio-inspired Information and Communication Technologies. BICT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-24202-2_11
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DOI: https://doi.org/10.1007/978-3-030-24202-2_11
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