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