Effects of the coordination number on H2O dissociation reaction on the surface of Zr5nO10n (n=4–9) nanoparticles: A DFT approach

2019 ◽  
Vol 44 (59) ◽  
pp. 31029-31040 ◽  
Author(s):  
Yucheng Xu ◽  
Ning Wang ◽  
Xiangyu Guo ◽  
Shiping Huang
Keyword(s):  
Coordination Number ◽  
Dissociation Reaction

Bond-length distributions for ions bonded to oxygen: Results for the lanthanides and actinides and discussion of the f-block contraction

2017 ◽  
Author(s):  
Olivier Charles Gagné
Keyword(s):  
Bond Length ◽  
Coordination Number ◽  
Lanthanide Ions ◽  
Lanthanide Contraction ◽  
Trivalent Lanthanide ◽  
Actinide Ions

Bond-length distributions have been examined for eighty-four configurations of the lanthanide ions and twenty-two configurations of the actinide ions bonded to oxygen. The lanthanide contraction for the trivalent lanthanide ions bonded to O<sup>2-</sup> is shown to vary as a function of coordination number and to diminish in scale with increasing coordination number.

Elasticity of colloidal gels: structural heterogeneity, floppy modes, and rigidity

Soft Matter ◽  
2021 ◽  
Author(s):  
D. Zeb Rocklin ◽  
Lilian C Hsiao ◽  
Megan E Szakasits ◽  
Michael J Solomon ◽  
Xiaoming Mao
Keyword(s):  
Coordination Number ◽  
Volume Fraction ◽  
Structural Parameters ◽  
Structural Heterogeneity ◽  
Colloidal Gels ◽  
Shear Moduli ◽  
Rheological Measurements

Rheological measurements of model colloidal gels reveal that large variations in the shear moduli as colloidal volume-fraction changes are not reflected by simple structural parameters such as the coordination number,...

scholarly journals Estimating Contact Force Chains Using Artificial Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6278
Author(s):  
Mengmeng Wu ◽  
Jianfeng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Granular Materials ◽  
Coordination Number ◽  
Contact Force ◽  
Force Chains ◽  
Granular System ◽  
Biaxial Test ◽  
Artificial Neural

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.

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