Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/4986
Título: Neural-network approach to modeling liquid crystals in complex confinement
Autor: Santos-Silva, T.
Teixeira, Paulo Ivo Cortez
Anquetil-Deck, C.
Cleaver, D. J.
Palavras-chave: Alignment
Data: Mar-2014
Editora: Amer Physical Society
Citação: SANTOS-SILVA; TEIXEIRA, Paulo Ivo Cortez; ANQUETIL-DECK, C.; CLEAVER, D. J. – Neural-network approach to modeling liquid crystals in complex confinement. Physical Review E. ISSN: 1539-3755. Vol. 89, nr. 5 (2014), Art. nr. 053316
Relatório da Série N.º: 053316
Resumo: Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.
Peer review: yes
URI: http://hdl.handle.net/10400.21/4986
Aparece nas colecções:ISEL - Física - Artigos

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