Assessment of the Thermal Conductivity of BN-C Nanostructures

Chemical and structural diversity present in hexagonal boron nitride ((h-BN) and graphene hybrid nanostructures provide new avenues for tuning various properties for their technological applications. In this paper we investigate the variation of thermal conductivity ($\kappa$) of hybrid graphene/h-BN nanostructures: stripe superlattices and BN (graphene) dots embedded in graphene (BN) are investigated using equilibrium molecular dynamics. To simulate these systems, we have parameterized a Tersoff type interaction potential to reproduce the ab initio energetics of the B-C and N-C bonds for studying the various interfaces that emerge in these hybrid nanostructures. We demonstrate that both the details of the interface, including energetic stability and shape, as well as the spacing of the interfaces in the material exert strong control on the thermal conductivity of these systems. For stripe superlattices, we find that zigzag configured interfaces produce a higher $\kappa$ in the direction parallel to the interface than the armchair configuration, while the perpendicular conductivity is less prone to the details of the interface and is limited by the $\kappa$ of h-BN. Additionally, the embedded dot structures, having mixed zigzag and armchair interfaces, affects the thermal transport properties more strongly than superlattices. Though dot radius appears to have little effect on the magnitude of reduction, we find that dot concentration (50% yielding the greatest reduction) and composition (embedded graphene dots showing larger reduction that h-BN dot) have a significant effect

Equilibrium Limit of Boundary Scattering in Carbon Nanostructures: Molecular Dynamics Calculations of Thermal Transport

It is widely known that graphene and many of its derivative nanostructures have exceedingly high reported thermal conductivities (up to 4000 W/mK at 300 K). Such attractive thermal properties beg the use of these structures in practical devices; however, to implement these materials while preserving transport quality, the influence of structure on thermal conductivity should be thoroughly understood. For graphene nanostructures, having average phonon mean free paths on the order of one micron, a primary concern is how size influences the potential for heat conduction. To investigate this, we employ a novel technique to evaluate the lattice thermal conductivity from the Green-Kubo relations and equilibrium molecular dynamics in systems where phonon-boundary scattering dominates heat flow. Specifically, the thermal conductivities of graphene nanoribbons and carbon nanotubes are calculated in sizes up to 3 microns, and the relative influence of boundary scattering on thermal transport is determined to be dominant at sizes less than 1 micron, after which the thermal transport largely depends on the quality of the nanostructure interface. The method is also extended to carbon nanostructures (fullerenes) where phonon confinement, as opposed to boundary scattering, dominates, and general trends related to the influence of curvature on thermal transport in these materials are discussed

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$O_2$. 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

Molecular Level Assessment of Thermal Transport and Thermoelectricity in Materials: From Bulk Alloys to Nanostructures

The ability to manipulate material response to dynamical processes depends on the extent of understanding of transport properties and their variation with chemical and structural features in materials. In this perspective, current work focuses on the thermal and electronic transport behavior of technologically important bulk and nanomaterials. Strontium titanate is a potential thermoelectric material due to its large Seebeck coefficient. Here, first principles electronic band structure and Boltzmann transport calculations are employed in studying the thermoelectric properties of this material in doped and deformed states. The calculations verified that excessive carrier concentrations are needed for this material to be used in thermoelectric applications. Carbon- and boron nitride-based nanomaterials also offer new opportunities in many applications from thermoelectrics to fast heat removers. For these materials, molecular dynamics calculations are used to evaluate lattice thermal transport. To do this, first, an energy moment term is reformulated for periodic boundary conditions and tested to calculate thermal conductivity from Einstein relation in various systems. The influences of the structural details (size, dimensionality) and defects (vacancies, Stone-Wales defects, edge roughness, isotopic disorder) on the thermal conductivity of C and BN nanostructures are explored. It is observed that single vacancies scatter phonons stronger than other type of defects due to unsatisfied bonds in their structure. In pristine states, BN nanostructures have 4-6 times lower thermal conductivity compared to C counterparts. The reason of this observation is investigated on the basis of phonon group velocities, life times and heat capacities. The calculations show that both phonon group velocities and life times are smaller in BN systems. Quantum corrections are also discussed for these classical simulations. The chemical and structural diversity that could be attained by mixing hexagonal boron nitride and graphene provide further avenues for tuning thermal and electronic properties. In this work, the thermal conductivity of hybrid graphene/hexagonal-BN structures: stripe superlattices and BN (graphene) dots embedded in graphene (BN) are studied. The largest reduction in thermal conductivity is observed at 50% chemical mixture in dot superlattices. The dot radius appears to have little effect on the magnitude of reduction around large concentrations while smaller dots are more influential at dilute systems

First-Principles Analysis of Defect-Mediated Li Adsorption on Graphene

To evaluate the possible utility of single layer graphene for applications in Li ion batteries, an extensive series of periodic density functional theory (DFT) calculations are performed on graphene sheets with both point and extended defects for a wide range of lithium coverages. Consistent with recent reports, it is found that Li adsorption on defect-free single layer graphene is not thermodynamically favorable compared to bulk metallic Li. However, graphene surfaces activated by defects are generally found to bind Li more strongly, and the interaction strength is sensitive to both the nature of the defects and their densities. Double vacancy defects are found to have much stronger interactions with Li as compared to Stone–Wales defects, and increasing defect density also enhances the interaction of the Stone–Wales defects with Li. Li interaction with one-dimensional extended defects on graphene is additionally found to be strong and leads to increased Li adsorption. A rigorous thermodynamic analysis of these data establishes the theoretical Li storage capacities of the defected graphene structures. In some cases, these capacities are found to approach, although not exceed, those of graphite. The results provide new insights into the fundamental physics of adsorbate interactions with graphene defects and suggest that careful defect engineering of graphene might, ultimately, provide anode electrodes of suitable capacity for lithium ion battery applications