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Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the as-fabricated microstructure, this work integrates experiments with a thermodynamic and kinetic modeling framework to probe the limits of the dispersoid sizes and number densities that can be achieved with powder bed fusion-laser beam. Bulk samples of a Ni-20Cr $+$ 1 wt.% Y$_2$O$_3$ alloy are fabricated using a range of laser power and scanning velocity combinations. Scanning transmission electron microscopy characterization is performed to quantify the dispersoid size distributions across the processing space. The smallest mean dispersoid diameter (29 nm) is observed at 300 W and 1200 mm/s, with a number density of 1.0$\times$10$^{20}$ m$^{-3}$. The largest mean diameter (72 nm) is observed at 200 W and 200 mm/s, with a number density of 1.5$\times$10$^{19}$ m$^{-3}$. Scanning electron microscopy suggests that a considerable fraction of the oxide added to the feedstock is lost during processing, due to oxide agglomeration and the ejection of oxide-rich spatter from the melt pool. After accounting for these losses, the model predictions for the dispersoid diameter and number density align with the experimental trends. The results suggest that the mechanism that limits the final number density is collision coarsening of dispersoids in the melt pool. The modeling framework is leveraged to propose processing strategies to limit dispersoid size and increase number density., Comment: Main text: 37 pages, 20 figures

In this study, we added vacancies adjacent to a Si/Ge interface to create a disordered structure. The structure was then relaxed using various strategies. We applied Procrustes shape analysis for disorder quantification and identifying different local atomic environments.

Feature extraction and a neural network model are applied to predict the defect types and concentrations in experimental TiO$_2$ samples. A dataset of TiO$_2$ structures with vacancies and interstitials of oxygen and titanium is built and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis, non-negative matrix factorization) and non-linear methods (autoencoder, convolutional neural network). The extracted features are used as the inputs to a neural network that maps the feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting the defect concentrations based on experimentally-measured TiO$_2$ PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting the defect concentrations. This model incorporates physical interpretability and predictability of material properties, enabling a more efficient material characterization process with scattering data.

A workflow is presented for performing pair distribution function (PDF) analysis of defected materials using structures generated from atomistic simulations. A large collection of structures, which differ in the types and concentrations of defects present, are obtained through energy minimization with an empirical interatomic potential. Each of the structures is refined against an experimental PDF. The structures with the lowest goodness of fit $R_w$ values are taken as being representative of the experimental structure. The workflow is applied to anatase titanium dioxide ($a$-TiO$_2$) and tetragonal zirconium dioxide ($t$-ZrO$_2$) synthesized in the presence of microwave radiation, a low temperature process that generates disorder. The results suggest that titanium vacancies and interstitials are the dominant defects in $a$-TiO$_2$, while oxygen vacancies dominate in $t$-ZrO$_2$. Analysis of the atomic displacement parameters extracted from the PDF refinement and mean squared displacements calculated from molecular dynamics simulations indicate that while these two quantities are closely related, it is challenging to make quantitative comparisons between them. The workflow can be applied to other materials systems, including nanoparticles.

With the objective to understand microscopic principles governing thermal energy flow in nanojunctions, we study phononic heat transport through metal-molecule-metal junctions using classical molecular dynamics (MD) simulations. Considering a single-molecule gold-alkanedithiol-gold junction, we first focus on aspects of method development and compare two techniques for calculating thermal conductance: (i) The Reverse Nonequilibrium MD (RNEMD) method, where heat is inputted and extracted at a constant rate from opposite metals. In this case, the thermal conductance is calculated from the nonequilibrium temperature profile that is created on the junction. (ii) The Approach-to-Equilibrium MD (AEMD) method, with the thermal conductance of the junction obtained from the equilibration dynamics of the metals. In both methods, simulations of alkane chains of growing size display an approximate length-independence of the thermal conductance, with calculated values matching computational and experimental studies. The RNEMD and AEMD methods offer different insights on thermal transport, and we discuss their relative benefits and shortcomings. Assessing the potential application of molecular junctions as thermal diodes, the alkane junctions are made spatially asymmetric by modifying their contact regions with the bulk, either by using distinct endgroups or by replacing one of the Au contacts by Ag. Anharmonicity is built into the system within the molecular force-field. Using the RNEMD method, we show that, while the temperature profile strongly varies (compared to the gold-alkanedithiol-gold junctions) due to these structural modifications, the thermal diode effect is inconsequential in these systems -- unless one goes to very large thermal biases. This finding suggests that one should seek molecules with considerable internal anharmonic effects for developing nonlinear thermal devices.

We present a framework for quantifying the uncertainty that results from the choice of exchange-correlation (XC) functional in predictions of phonon properties and thermal conductivity that use density functional theory (DFT) to calculate the atomic force constants. The energy ensemble capabilities of the BEEF-vdW XC functional are first applied to determine an ensemble of interatomic force constants, which are then used as inputs to lattice dynamics calculations and a solution of the Boltzmann transport equation. The framework is applied to isotopically-pure silicon. We find that the uncertainty estimates bound property predictions (e.g., phonon dispersions, specific heat, thermal conductivity) from other XC functionals and experiments. We distinguish between properties that are correlated with the predicted thermal conductivity [e.g., the transverse acoustic branch sound speed ($R^2=0.89$) and average Gr\"uneisen parameter ($R^2=0.85$)] and those that are not [e.g., longitudinal acoustic branch sound speed ($R^2=0.23$) and specific heat ($R^2=0.00$)]. We find that differences in ensemble predictions of thermal conductivity are correlated with the behavior of phonons with mean free paths between $100$ and $300$ nm. The framework systematically accounts for XC uncertainty in phonon calculations and should be used whenever it is suspected that the choice of XC functional is influencing physical interpretations., Comment: 20 pages, 5 figures, 13 pages of Supplemental Material

An algorithm for mapping the true phonon modes of a film, which are defined by a two-dimensional (2D) Brillouin zone, to the modes of the corresponding bulk material, which are defined by a three-dimensional (3D) Brillouin zone, is proposed. The algorithm is based on normal mode decomposition and is inspired by the observation that the atomic motions generated by the 2D eigenvectors lead to standing-wave-like behaviors in the cross-plane direction. It is applied to films between two and ten unit cells thick built from Lennard-Jones (LJ) argon, whose bulk is isotropic, and graphene, whose bulk (graphite) is anisotropic. For LJ argon, the density of states deviates from that of the bulk as the film gets thinner due to phonon frequencies that shift to lower values. This shift is a result of transverse branch splitting due to the film's anisotropy and the emergence of a quadratic acoustic branch. As such, while the mapping algorithm works well for the thicker LJ argon films, it does not perform as well for the thinner films as there is a weaker correspondence between the 2D and 3D modes. For graphene, the density of states of even the thinnest films closely matches that of graphite due to the inherent anisotropy, except for a small shift at low frequency. As a result, the mapping algorithm works well for all thicknesses of the graphene films, indicating a strong correspondence between the 2D and 3D modes.

Thermal transport by phonons in films with thicknesses of less than 10 nm is investigated in a soft system (Lennard-Jones argon) and a stiff system (Tersoff silicon) using two-dimensional lattice dynamics calculations and the Boltzmann transport equation. This approach uses a unit cell that spans the film thickness, which removes approximations related to the finite cross-plane dimension required in typical three-dimensional-based approaches. Molecular dynamics simulations are performed to obtain finite-temperature structures for the lattice dynamics calculations and to predict thermal conductivity benchmarks. Thermal conductivity decreases with decreasing film thickness until the thickness reaches four unit cells (2.1 nm) for argon and three unit cells (1.6 nm) for silicon. With a further decrease in film thickness, thermal conductivity plateaus in argon while it increases in silicon. This unexpected behavior, which we identify as a signature of phonon confinement, is a result of an increased contribution from low-frequency phonons, whose density of states increases as the film thickness decreases. Phonon mode-level analysis suggests that confinement effects emerge below thicknesses of ten unit cells (5.3 nm) for argon and six unit cells (3.2 nm) for silicon. Thermal conductivity predictions based on the bulk phonon properties combined with a boundary scattering model do not capture the low thickness behavior. To match the two-dimensional lattice dynamics and molecular dynamics predictions for larger thicknesses, the three-dimensional lattice dynamics calculations require a finite specularity parameter that in some cases approaches unity. These findings point to the challenges associated with interpreting experimental thermal conductivity measurements of ultrathin silicon films, where surface roughness and a native oxide layer impact phonon transport.

Accurate prediction of molecular vibrational frequencies is important to identify spectroscopic signatures and reaction thermodynamics. In this work, we develop a method to quantify uncertainty associated with density functional theory predicted harmonic vibration frequencies utilizing the built-in error estimation capabilities of the BEEF-vdW exchange-correlation functional. The method is computationally efficiency by estimating the uncertainty at nearly the same computational cost as a single vibrational frequency calculation. We demonstrate the utility and robustness of the method by showing that the uncertainty estimates bounds the self-consistent calculations of six exchange correlation functionals for small molecules, rare gas dimers, and molecular complexes from the S22 dataset. Ten rare-gas dimers and the S22 dataset of molecular complexes provide a rigorous test as they are systems with complicated vibrational motion and non-covalent interactions. Using coefficient of variation as a uncertainty metric, we find that modes involving bending or torsional motion and those dominated by non-covalent interactions are found to have higher uncertainty in their predicted frequencies than covalent stretching modes. Given the simplicity of the method, we believe that this method can be easily adopted and should form a routine part of DFT-predicted harmonic frequency analysis., Comment: 32 pages, 40 pages of Supporting Information

Organolead halide perovskites are a family of hybrid organic-inorganic compounds whose remarkable optoelectronic properties have been under intensive scrutiny in recent years. Here we use inelastic X-ray scattering to study low-energy lattice excitations in single crystals of methylammonium lead iodide and bromide perovskites. Our findings confirm the displacive nature of the cubic-to-tetragonal phase transition, which is further shown, using neutron and x-ray diffraction, to be close to a tricritical point. Lastly, we detect quasistatic symmetry-breaking nanodomains persisting well into the high-temperature cubic phase, possibly stabilized by local defects. These findings reveal key structural properties of these materials, and also bear important implications for carrier dynamics across an extended temperature range relevant for photovoltaic applications., Comment: Accepted to Physical Review B