Bulk Metallic System Modeling of Metal Hydride Dimer and Trimer Nanoclusters

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Abstract:

An artificial neural network (ANN) has been used to predict the experimental values of: entropy, enthalpy, hydriding temperature at one atmosphere of pressure, hydriding pressure at 25 °C, plateau slope, and percent weight of hydrogen reversibly adsorbed/absorbed and desorbed for metal hydrides. Computational modeling methods, using density functional theory (DFT), calculated the minimum energy, dipole moment, nuclear repulsion energy, as well as the energies of the HOMOs and LUMOs of the metal clusters of interest. The predicted ANN values were compared to the experimental data. As additional experimental data is added to the input, the network tends to converge to a correct prediction for most of the properties evaluated. However, as additional experimental data is not always available, our best results were found for the percent weight of hydrogen stored for metal nanoclusters using only DFT parameters.

Keywords: AB INITIO MODELING; ARTIFICIAL INTELLIGENCE; METAL HYDRIDES; METAL NANOCLUSTERS; NEURAL NETWORKS

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/jctn.2010.1460

Publication date: June 1, 2010

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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