86 research outputs found
Transitional intermittency in boundary layers subjected to pressure gradient
Results are reported from an extensive series of experiments on boundary layers in which the location of pressure gradient and transition onset could be varied almost independently, by judicious use of tunnel wall liners and transition-fixing devices. The experiments show that the transition zone is sensitive to the pressure gradient especially near onset, and can be significantly asymmetric; no universal similarity appears valid in general. Observed intermittency distributions cannot be explained on the basis of the hypothesis, often made, that the spot propagates at speeds proportional to the local free-stream velocity but is otherwise unaffected by the pressure gradient
Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces
Tantalum, tantalum oxide and their hetero-interfaces are of tremendous
technological interest in several applications spanning electronics, thermal
management, catalysis and biochemistry. For example, local oxygen stoichiometry
variation in TaOx memristors comprising of metallic (Ta) and insulating oxide
(Ta2O5) have been shown to result in fast switching on the sub-nanosecond
timescale over a billion cycles, relevant to neuromorphic computation. Despite
its broad importance, an atomistic scale understanding of oxygen stoichiometry
variation across Ta/TaOx hetero-interfaces, such as during early stages of
oxidation and oxide growth, is not well understood. This is mainly due to the
lack of a variable charge interatomic potential model for tantalum oxides that
can accurately describe the ionic interactions in the metallic (Ta) and oxide
(TaOx) environment as well as at their interfaces. To address this challenge,
we introduce a charge transfer ionic potential (CTIP) model for Ta/Ta-oxide
system by training against lattice parameters, cohesive energies, equations of
state, and elastic properties of various experimentally observed Ta2O5
polymorphs. The best set of CTIP parameters are determined by employing a
single-objective global optimization scheme driven by genetic algorithms
followed by local Simplex optimization. Our newly developed CTIP potential
accurately predicts structure, thermodynamics, energetic ordering of
polymorphs, as well as elastic and surface properties of both Ta and Ta2O5, in
excellent agreement with DFT calculations and experiments. We employ our newly
parameterized CTIP potential to investigate the early stages of oxidation of Ta
at different temperatures and atomic/molecular nature of the oxidizing species
Comparing optimization strategies for force field parameterization
Classical molecular dynamics (MD) simulations enable modeling of materials
and examination of microscopic details that are not accessible experimentally.
The predictive capability of MD relies on the force field (FF) used to describe
interatomic interactions. FF parameters are typically determined to reproduce
selected material properties computed from density functional theory (DFT)
and/or measured experimentally. A common practice in parameterizing FFs is to
use least-squares local minimization algorithms. Genetic algorithms (GAs) have
also been demonstrated as a viable global optimization approach, even for
complex FFs. However, an understanding of the relative effectiveness and
efficiency of different optimization techniques for the determination of FF
parameters is still lacking. In this work, we evaluate various FF parameter
optimization schemes, using as example a training data set calculated from DFT
for different polymorphs of Ir. The Morse functional form is chosen for
the pairwise interactions and the optimization of the parameters against the
training data is carried out using (1) multi-start local optimization
algorithms: Simplex, Levenberg-Marquardt, and POUNDERS, (2) single-objective
GA, and (3) multi-objective GA. Using random search as a baseline, we compare
the algorithms in terms of reaching the lowest error, and number of function
evaluations. We also compare the effectiveness of different approaches for FF
parameterization using a test data set with known ground truth (i.e generated
from a specific Morse FF). We find that the performance of optimization
approaches differs when using the Test data vs. the DFT data. Overall, this
study provides insight for selecting a suitable optimization method for FF
parameterization, which in turn can enable more accurate prediction of material
properties and chemical phenomena
Habituation based synaptic plasticity and organismic learning in a quantum perovskite
A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: A key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.United States. Army Research Office (Grant W911NF-16-1-0289)United States. Air Force Office of Scientific Research (Grant FA9550-16-1-0159)United States. Army Research Office (Grant W911NF-16-1-0042
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