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Compositional Tuning of Ultrathin Surface Oxides on Metal and Alloy Substrates Using Photons: Dynamic Simulations and Experiments
We report on the ability to modify the structure and composition of ultrathin oxides grown on Ni and Ni-Al alloy surfaces at room temperature utilizing photon illumination. We find that the nickel-oxide formation is enhanced in the case of oxidation under photo-excitation. The enhanced oxidation kinetics of nickel in 5% Ni-Al alloy is corroborated by experimental and simulation studies of natural and photon-assisted oxide growth on pure Ni(100) surfaces. In case of pure Ni substrates, combined x-ray photoelectron spectroscopy analysis, and atomic force microscope current mapping support the deterministic role of the structure of nickel passive-oxide films on their nanoscale corrosion resistance. Atomistic simulations involving dynamic charge transfer predict that the applied electric field overcomes the activation-energy barrier for ionic migration, leading to enhanced oxygen incorporation into the oxide, enabling us to tune the mixed-oxide composition at atomic length scales. Atomic scale control of ultrathin oxide structure and morphology in the case of pure substrates as well as compositional tuning of complex oxide in the case of alloys leads to excellent passivity as verified from potentiodynamic polarization experiments.Engineering and Applied SciencesPhysic
Machine learning for classifying and interpreting coherent X-ray speckle patterns
Speckle patterns produced by coherent X-ray have a close relationship with
the internal structure of materials but quantitative inversion of the
relationship to determine structure from speckle patterns is challenging. Here,
we investigate the link between coherent X-ray speckle patterns and sample
structures using a model 2D disk system and explore the ability of machine
learning to learn aspects of the relationship. Specifically, we train a deep
neural network to classify the coherent X-ray speckle patterns according to the
disk number density in the corresponding structure. It is demonstrated that the
classification system is accurate for both non-disperse and disperse size
distributions
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
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