65 research outputs found
Realistic time-scale fully atomistic simulations of surface nucleation of dislocations in pristine nanopillars
We use our recently proposed accelerated dynamics algorithm (Tiwary and van de Walle, 2011) to calculate temperature and stress dependence of activation free energy for surface nucleation of dislocations in pristine Gold nanopillars under realistic loads. While maintaining fully atomistic resolution, we achieve the fraction of a second time-scale regime. We find that the activation free energy depends significantly and non-linearly on the driving force (stress or strain) and temperature, leading to very high activation entropies. We also perform compression tests on Gold nanopillars for strain-rates varying between 7 orders of magnitudes, reaching as low as 10^3/s. Our calculations bring out the perils of high strain-rate Molecular Dynamics calculations: we find that while the failure mechanism for compression of Gold nanopillars remains the same across the entire strain-rate range, the elastic limit (defined as stress for nucleation of the first dislocation) depends significantly on the strain-rate. We also propose a new methodology that overcomes some of the limits in our original accelerated dynamics scheme (and accelerated dynamics methods in general). We lay out our methods in sufficient details so as to be used for understanding and predicting deformation mechanism under realistic driving forces for various problems
Hybrid deterministic and stochastic approach for efficient atomistic simulations at long time scales
We propose a hybrid deterministic and stochastic approach to achieve extended
time scales in atomistic simulations that combines the strengths of molecular
dynamics (MD) and Monte Carlo (MC) simulations in an easy-to-implement way. The
method exploits the rare event nature of the dynamics similar to most current
accelerated MD approaches but goes beyond them by providing, without any
further computational overhead, (a) rapid thermalization between infrequent
events, thereby minimizing spurious correlations, and (b) control over accuracy
of time-scale correction, while still providing similar or higher boosts in
computational efficiency. We present two applications of the method: (a)
Vacancy-mediated diffusion in Fe yields correct diffusivities over a wide range
of temperatures and (b) source-controlled plasticity and deformation behavior
in Au nanopillars at realistic strain rates (10^4/s and lower), with excellent
agreement with previous theoretical predictions and in situ high-resolution
transmission electron microscopy observations. The method gives several
orders-of-magnitude improvements in computational efficiency relative to
standard MD and good scalability with the size of the system.Comment: 4 pages, 2 figures. Corrected logarithm base in figures 2 and
Thermodynamically Optimized Machine-learned Reaction Coordinates for Hydrophobic Ligand Dissociation
Ligand unbinding is mediated by the free energy change, which has intertwined
contributions from both energy and entropy. It is important but not easy to
quantify their individual contributions. We model hydrophobic ligand unbinding
for two systems, a methane particle and a C60 fullerene, both unbinding from
hydrophobic pockets in all-atom water. By using a modified deep learning
framework, we learn a thermodynamically optimized reaction coordinate to
describe hydrophobic ligand dissociation for both systems. Interpretation of
these reaction coordinates reveals the roles of entropic and enthalpic forces
as ligand and pocket sizes change. Irrespective of the contrasting roles of
energy and entropy, we also find that for both the systems the transition from
the bound to unbound states is driven primarily by solvation of the pocket and
ligand, independent of ligand size. Our framework thus gives useful
thermodynamic insight into hydrophobic ligand dissociation problems that are
otherwise difficult to glean.Comment: 27 pages; 5 figure
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