Machine learning and dissimilarity analysis for surface-assisted molecular self-assembly

Daniel Packwood
Kyoto University

Bottom-up fabrication refers to the spontaneous formation of new nanomaterials via self-assembly of molecule precursors. In order to successfully assemble a specific nanomaterial via bottom-up fabrication, precursor molecules that interact and align correctly with each other during the self-assembly process must be first predicted. This presentation will introduce a new method for modeling the self-assembly of organic precursor molecules on metal surfaces. This method uses an Ising-like model, in which the energy function is constructed from first-principles data via machine learning. Markov chain Monte Carlo is then used to find the equilibrium states of the model [1, 2, 3]. Finally, the application of a dissimilarity analysis technique to the model will be described. This analysis quantifies how self-assembly depends upon the chemical properties of the precursor molecule, and can help identify appropriate precursor molecules for bottom-up fabrication of target nanomaterials.

[1] Daniel Packwood, Patrick Han, and Taro Hitosugi. Nat. Commun. 8, 2017, 14463
[2] Daniel Packwood and Taro Hitosugi. Appl. Phys. Express. 10, 2017, 065502
[3] Daniel Packwood, Patrick Han, Taro Hitosugi. Roy. Soc. Open Sci. 3, 2016, 150681

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