7,706 research outputs found
A scalable parallel Monte Carlo algorithm for atomistic simulations of precipitation in alloys
We present an extension of the semi-grandcanonical (SGC) ensemble that we
refer to as the variance-constrained semi-grandcanonical (VC-SGC) ensemble. It
allows for transmutation Monte Carlo simulations of multicomponent systems in
multiphase regions of the phase diagram and lends itself to scalable
simulations on massively parallel platforms. By combining transmutation moves
with molecular dynamics steps structural relaxations and thermal vibrations in
realistic alloys can be taken into account. In this way, we construct a robust
and efficient simulation technique that is ideally suited for large-scale
simulations of precipitation in multicomponent systems in the presence of
structural disorder. To illustrate the algorithm introduced in this work, we
study the precipitation of Cu in nanocrystalline Fe.Comment: 12 pages; 10 figure
The ReaxFF reactive force-field : development, applications and future directions
The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method
A Tutorial on Advanced Dynamic Monte Carlo Methods for Systems with Discrete State Spaces
Advanced algorithms are necessary to obtain faster-than-real-time dynamic
simulations in a number of different physical problems that are characterized
by widely disparate time scales. Recent advanced dynamic Monte Carlo algorithms
that preserve the dynamics of the model are described. These include the
-fold way algorithm, the Monte Carlo with Absorbing Markov Chains (MCAMC)
algorithm, and the Projective Dynamics (PD) algorithm. To demonstrate the use
of these algorithms, they are applied to some simplified models of dynamic
physical systems. The models studied include a model for ion motion through a
pore such as a biological ion channel and the metastable decay of the
ferromagnetic Ising model. Non-trivial parallelization issues for these dynamic
algorithms, which are in the class of parallel discrete event simulations, are
discussed. Efforts are made to keep the article at an elementary level by
concentrating on a simple model in each case that illustrates the use of the
advanced dynamic Monte Carlo algorithm.Comment: 53 pages, 17 figure
Diagrammatic Coupled Cluster Monte Carlo
We propose a modified coupled cluster Monte Carlo algorithm that
stochastically samples connected terms within the truncated
Baker--Campbell--Hausdorff expansion of the similarity transformed Hamiltonian
by construction of coupled cluster diagrams on the fly. Our new approach --
diagCCMC -- allows propagation to be performed using only the connected
components of the similarity-transformed Hamiltonian, greatly reducing the
memory cost associated with the stochastic solution of the coupled cluster
equations. We show that for perfectly local, noninteracting systems, diagCCMC
is able to represent the coupled cluster wavefunction with a memory cost that
scales linearly with system size. The favorable memory cost is observed with
the only assumption of fixed stochastic granularity and is valid for arbitrary
levels of coupled cluster theory. Significant reduction in memory cost is also
shown to smoothly appear with dissociation of a finite chain of helium atoms.
This approach is also shown not to break down in the presence of strong
correlation through the example of a stretched nitrogen molecule. Our novel
methodology moves the theoretical basis of coupled cluster Monte Carlo closer
to deterministic approaches.Comment: 31 pages, 6 figure
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