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Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations.
Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via voronoi tessellations.
Faber, F., Lindmaa, A., von Lilienfeld, O. A. & Armiento, R. Crystal structure representations for machine learning models of formation energies.
The microscopic sequences are presented here as descriptive models of formation processes.
A variety of models of formation have been put forward, involving silicification in both pedogenic and non-pedogenic settings.
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The statistical model of formation of this film was successfully simulated by molecular dynamics.
The article describes the ontological modeling of formation manufacturing work flow instructions.
Based on the investigated results, a model of formation of cylindrulite is proposed.
The model of formation of these nanostructures (TNT) has been also proposed.
The model of formation and growth of stoichiometric (Al2O3)n clusters during the combustion of aluminized fuels has been developed.
Development of a reduced model of formation reactions in Zr-Al nanolaminates.
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