scholarly journals Atomic-scale modeling of the thermodynamic and kinetic properties of dilute alloys driven by forced atomic relocations

2019 ◽  
Vol 100 (22) ◽  
Author(s):  
Liangzhao Huang ◽  
Thomas Schuler ◽  
Maylise Nastar
1998 ◽  
Vol 62 (5) ◽  
pp. 581-583
Author(s):  
Simon A. T. Redfern

How can the equilibrium and non-equilibrium thermodynamics of minerals be understood from their atomic-scale structural features? How can they be predicted, simply from models for the forces between atoms? Advances in analytical theory, statistical mechanics, experimental solid-state science, computational power, and the sophistication of a mineralogical approach that brings all of these together, means that these questions, once imponderable, are now realistically tractable. These questions have been exercising the minds of mineralogists over the last decade or so, and have motivated many developments in the science. Acting as way-markers along the path, there are a number of publications which have followed from meetings where these questions have been addressed. It is now twelve years since the publication of Microscopic to Macroscopic, an edition of Reviews in Mineralogy (Kieffer and Navrotsky, 1985) that sought to identify the fundamental controls on the bulk properties of minerals in terms of their atomic-scale characteristics.


2021 ◽  
pp. 117098
Author(s):  
Jian Luo ◽  
Binghui Deng ◽  
K. Deenamma Vargheese ◽  
Adama Tandia ◽  
Steven E. DeMartino ◽  
...  

Nano Letters ◽  
2008 ◽  
Vol 8 (12) ◽  
pp. 4205-4209 ◽  
Author(s):  
Chumin Wang ◽  
Fernando Salazar ◽  
Vicenta Sánchez

2019 ◽  
Vol 73 (12) ◽  
pp. 972-982 ◽  
Author(s):  
Félix Musil ◽  
Michele Ceriotti

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condensed-phase systems. This short review summarizes recent progress in the field, focusing in particular on the problem of representing an atomic configuration in a mathematically robust and computationally efficient way. We also discuss some of the regression algorithms that have been used to construct surrogate models of atomic-scale properties. We then show examples of how the optimization of the machine-learning models can both incorporate and reveal insights onto the physical phenomena that underlie structure–property relations.


1999 ◽  
Vol 578 ◽  
Author(s):  
T. Vegge ◽  
O. B. Pedersen ◽  
T. Leffers ◽  
K. W. Jacobsen

AbstractUsing atomistic simulations we investigate the annihilation of screw dislocation dipoles in Cu. In particular we determine the influence of jogs on the annihilation barrier for screw dislocation dipoles. The simulations involve energy minimizations, molecular dynamics, and the Nudged Elastic Band method. We find that jogs on screw dislocations substantially reduce the annihilation barrier, hence leading to an increase in the minimum stable dipole height.


2013 ◽  
Vol 5 (11) ◽  
pp. 1147-1154 ◽  
Author(s):  
C. Arcangeli ◽  
I. Borriello ◽  
G. Gianese ◽  
M. Celino ◽  
P. Morales

2004 ◽  
Vol 40 (4) ◽  
pp. 2143-2145 ◽  
Author(s):  
S. Mukherjee ◽  
D. Litvinov ◽  
S. Khizroev
Keyword(s):  

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