Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces

Sasikumar, Kiran; Narayanan, Badri; Cherukara, Mathew J.; Kınacı, Alper; Şen, Fatih; Gray, Stephen K.; Chan, Maria K. Y.; Sankaranarayanan, Subramanian K. R. S.

doi:10.1021/acs.chemmater.7b00312

Cited by 26 publications

(26 citation statements)

References 70 publications

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“…Furthermore, noticeable charge transfer and intermixing at oxide heterointerfaces result in the occurrence of a variety of point defects in conjunction with structural defects, which further convolutes the interface structure. Development of charge transfer ionic potentials for oxides, reactive force‐fields, bond‐valence interatomic potentials, and second‐nearest‐neighbor modified embedded‐atom method are prospective approaches that could address the complex charge transfer and related atomic‐scale processes at semi‐coherent oxide heterointerfaces, further assisting in comprehending the actual atomic and chemical structure of the interface. As recently demonstrated by Uberuaga and co‐workers, albeit for a heterointerface between metal and metal oxide, one promising strategy to investigate the structure of misfit dislocations at oxide heterostructures is to strain the film and the substrate so as to keep the supercell size tractable, while still incorporating the full misfit dislocation structure in the DFT supercell.…”

Section: Discussionmentioning

confidence: 99%

Beyond Coherent Oxide Heterostructures: Atomic‐Scale Structure of Misfit Dislocations

Dholabhai

Uberuaga

2019

Advcd Theory and Sims

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Nanoscale design of complex oxide heterostructures and thin films is imperative as they have significant promise in novel technological applications. A coherent interface is formed in oxide heterostructures with small mismatches, and the lattice mismatch is completely compensated by elastic strain. In semi-coherent oxide heterostructures, when an epitaxial layer is grown on the substrate above the critical thickness of the film, misfit dislocations are formed to mitigate the strain between the two materials with dissimilar lattice constants. Key properties of semi-coherent oxide heterostructures are influenced or even controlled by the presence of misfit dislocations. Therefore, it is critical to understand the atomic-scale structure of semi-coherent oxide heterostructures, specifically the structure of misfit dislocations that are ubiquitous at such heterointerfaces. Numerous state-of-the-art experiments have reported emergent phenomena at semi-coherent oxide heterostructures, wherein misfit dislocations play a crucial role. However, their atomic-scale and nanoscale structure is not always discernable from experiments. Due to large system sizes, computational studies dedicated to examining misfit dislocations in semi-coherent oxide heterostructures are still in their infancy. This review aims to summarize the recent advancements and challenges involved in computational studies elucidating the atomic-scale structure of misfit dislocations in semi-coherent oxide heterostructures and motivate future computational efforts.

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Section: Discussionmentioning

confidence: 99%

Beyond Coherent Oxide Heterostructures: Atomic‐Scale Structure of Misfit Dislocations

Dholabhai

Uberuaga

2019

Advcd Theory and Sims

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“…Classical molecular dynamics simulations of the cubic, tetragonal and monoclinic phases of HfO 2 were carried out using LAMMPS (Sandia National Laboratory, Albuquerque, NM, USA; version 16 February 2016) [ 26 ]. The simulations were performed utilizing a recently developed LAMMPS implementation of the modified charge transfer potential model (CTIP) potential developed for multiple metal oxides [ 27 , 28 , 29 ]. Additional details regarding the CTIP potential are included in Appendix A .…”

Section: Methodsmentioning

confidence: 99%

The Structure of Liquid and Amorphous Hafnia

et al. 2017

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Understanding the atomic structure of amorphous solids is important in predicting and tuning their macroscopic behavior. Here, we use a combination of high-energy X-ray diffraction, neutron diffraction, and molecular dynamics simulations to benchmark the atomic interactions in the high temperature stable liquid and low-density amorphous solid states of hafnia. The diffraction results reveal an average Hf–O coordination number of ~7 exists in both the liquid and amorphous nanoparticle forms studied. The measured pair distribution functions are compared to those generated from several simulation models in the literature. We have also performed ab initio and classical molecular dynamics simulations that show density has a strong effect on the polyhedral connectivity. The liquid shows a broad distribution of Hf–Hf interactions, while the formation of low-density amorphous nanoclusters can reproduce the sharp split peak in the Hf–Hf partial pair distribution function observed in experiment. The agglomeration of amorphous nanoparticles condensed from the gas phase is associated with the formation of both edge-sharing and corner-sharing HfO6,7 polyhedra resembling that observed in the monoclinic phase.

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“…In ref , we use our ML framework to introduce a charge transfer ionic potential (CTIP) model for Ta/TaO x system that can describe the thermophysical, structural, and surface properties of Ta and the various Ta 2 O 5 polymorphs, as well as their interfaces.…”

Section: Case Studies (Model Selection Nature Of the Training Data Tr...mentioning

confidence: 99%

“…Performance of our ML trained CTIP-EAM potential model with DFT calculations. The equations of state for (a) monoclinic, (b) hexagonal, and (c) orthorhombic Ta 2 O 5 calculated using the EAM + Qeq parameters developed in ref (solid blue squares) and DFT calculations (solid red circles). Solid lines correspond to the Murnaghan fit.…”

Section: Case Studies (Model Selection Nature Of the Training Data Tr...mentioning

confidence: 99%

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

et al. 2019

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The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle selfassembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in realtime 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our datadriven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to twodimensional (2D) materials.

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Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces

Cited by 26 publications

References 70 publications

Beyond Coherent Oxide Heterostructures: Atomic‐Scale Structure of Misfit Dislocations

Beyond Coherent Oxide Heterostructures: Atomic‐Scale Structure of Misfit Dislocations

The Structure of Liquid and Amorphous Hafnia

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

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