Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces
Source abstract
Abstract
Heterostructures of tantalum and its oxide are of tremendous technological interest for myriad technological applications, including electronics, thermal management, catalysis, and biochemistry. In particular, local oxygen stoichiometry variation in TaO x memristors comprising thermodynamically stable metallic (Ta) and insulating oxide (Ta 2 O 5 ) have been shown to result in fast switching on the subnanosecond time scale over a billion cycles. This rapid switching opens up the potential for advanced functional platforms such as stateful logic operations and neuromorphic computation. Despite its broad importance, an atomistic scale understanding of oxygen stoichiometry variation across Ta/TaO x heterointerfaces, such as during early stages of oxidation and oxide growth, is not well understood. This is mainly due to the lack of a unified interatomic potential model for tantalum oxides that can accurately describe metallic (Ta) and ionic (TaO x ) as well as mixed (Ta/TaO x interfaces) bonding environments simultaneously. To address this challenge, we introduce a Charge Transfer Ionic Potential (CTIP) model for Ta/Ta-oxide system by training the model against lattice parameters, cohesive energies, equations of state (EOS), elastic properties, and surface energies of the various experimentally observed Ta 2 O 5 polymorphs (hexagonal, orthorhombic, and monoclinic) obtained from density functional theory (DFT) calculations. The best CTIP parameters are determined by employing a global optimization scheme driven by genetic algorithms followed by local simplex optimization. Our newly developed CTIP potential accurately predicts structure, thermodynamics, and energetic ordering of polymorphs, as well as elastic and surface properties of both Ta and Ta 2 O 5, in excellent agreement with DFT calculations and experiments. We employ our newly parametrized CTIP potential to investigate the early stages of oxidation and atomic scale mechanisms associated with oxide growth on the Ta surface at various temperatures. The CTIP potential developed in this work is an invaluable tool to investigate atomic-scale mechanisms and transport phenomena underlying the response of Ta/TaO x interfaces to external stimuli (e.g, temperature, pressure, strain, electric field, etc.), as well as other interesting dynamical phenomena including the physics of switching dynamics in TaO x based memristors and neuromorphic devices.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Machine Learning in Materials Science · Electrocatalysts for Energy Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois 60439, United States
X-ray Sciences Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
Computation Institute, University of Chicago, Chicago, Illinois 60637, United States
Funding
Argonne National Laboratory
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