Achieving a uniform distribution of oxide particles is a critical aspect of additive manufacturing of oxide dispersion-strengthened (ODS) alloys. In this study, we systematically varied the laser power, scanning velocity, and hatch spacing to study their impact on the oxide distribution in a Ni-20Cr + 0.8% mass fraction Y2O3 ODS alloy fabricated by powder bed fusion - laser beam (PBF-LB). The results show that melt pool overlap (defined as 1 − H/W, where H is hatch spacing and W is melt pool width) strongly influences the entrapment of micrometer-scale oxide inclusions but has an insignificant effect on the size and number density of nanometer-scale dispersoids. The area fraction of micrometer-scale inclusions decreases by an order of magnitude as overlap increases from 33% to 56%. At overlaps near 70%, we observed heat accumulation-related defects such as cracking and geometric distortion. These results suggest that an optimal hatch spacing exists (corresponding to 48% to 58% overlap) that minimizes micrometer-scale inclusions while avoiding excessive heat accumulation. In contrast, nanometer-scale dispersoids are largely unaffected by the melt pool overlap, exhibiting mean diameters of 24 nm to 29 nm and number densities of 1 × 1020 m−3 to 2 × 1020 m−3 across the range of processing conditions studied in this work. These findings establish the critical role of melt pool overlap in controlling inclusion entrapment in ODS alloys processed by PBF-LB, supporting the development of processing strategies.

Understanding the anharmonic phonon properties of crystal compounds-such as phonon lifetimes and thermal conductivities-is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities-both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Anisotropic thermal transport was measured in imine-linked two-dimensional polymer (2DP) films that were prepared by interfacial polymerization. Measurements of both in-plane (k∥) and cross-plane (k⊥) thermal conductivities relied on preparing free-standing 2DP films that were readily transferred for different measurement configurations. We polymerized two 2DP (Per-PDA and TAPPy-PDA) films at a liquid-liquid interface. These polycrystalline, imine-linked 2DP films are 100-200 nm in thickness and were measured by frequency domain thermoreflectance to extract k⊥ and a suspended calorimetric platform technique to evaluate k∥. We find that k∥ is larger than k⊥ in both materials at room temperature, leading to anisotropy ratios (k∥/k⊥) as high as 2.3. We attribute this behavior to the fact that the stiff, in-plane covalent bonds of 2DPs transport heat more effectively than the flexible, supramolecular cross-plane interactions. Variable-temperature measurements revealed a positive correlation between temperature and thermal conductivity, which we attribute to phonon scattering from grain boundaries and defects in the polycrystalline 2DP films. Molecular dynamics simulations of pristine crystals predict larger thermal conductivities and anisotropy ratios exceeding 7. The simulations suggest that as higher quality 2DP films become available, higher thermal conductivities and anisotropy ratios will also manifest.

Over the past decades, our understanding of thermal transport in amorphous materials has predominantly relied on the inherently harmonic Allen-Feldman theory, which has been found to be insufficient. In this study, the Wigner transport formalism is adopted to explicitly account for anharmonicity. In studying the thermal transport in amorphous silicon, the results highlight that amorphous materials are not generally computationally equivalent to crystals with disordered primitive cells. A method that leverages the properties of the two-mode terms in the Wigner transport formalism is proposed to predict the bulk thermal conductivity of amorphous materials using finite-size models. In doing so, the need for mode classification schemes required in the Allen-Feldman theory is eliminated, and similarities are discovered between the two-mode terms and the carriers commonly used to describe thermal transport in amorphous materials, i.e., propagons, diffusons, and locons. Two competing trends are identified that shed light on the recently discovered anomalous decrease in the high-temperature thermal conductivity in some amorphous materials.

An accurate formula for the atomic heat flux that can be used in molecular dynamics (MD) simulations driven by machine learning potentials is derived and discussed. The equivalence of the Torii and Fan atomic heat flux formulas for any two- or many-body potential is first demonstrated. For copper and silicon modeled with three machine learning potentials [Spectral Neighbor Analysis Potential (SNAP), atomic cluster expansion potential, and moment tensor potential], the default heat flux formula implemented in the large-scale atomic/molecular massively parallel simulator is shown to over- or underestimate the lattice thermal conductivity compared with the accurate formula, with no systematic error based on the material or potential. The accuracy of the heat flux formula and its implementation are further demonstrated by comparing the temperature dependence of the lattice thermal conductivity of copper modeled with a SNAP potential with that obtained with anharmonic lattice dynamics calculations by solving the phonon Boltzmann transport equation incorporating up to four-phonon scattering. This study will facilitate accurate thermal conductivity calculations using MD simulations with machine learning potentials.

We develop and validate a lattice dynamics framework to include anharmonic effects in the calculation of mode-level phonon heat capacities. To capture anharmonicity, the phonons are renormalized using a temperaturedependent effective potential and a proposed approach based on instantaneous normal modes. Ground-truth total heat capacities are obtained from molecular dynamics simulations. For Lennard-Jones argon (Stillinger-Weber silicon), the deviation of the potential energy contribution to the total heat capacity from the harmonic DulongPetit law is -12% (+16%) at the highest studied temperature of 80 K (1300 K). The mode heat capacities from the lattice dynamics calculations are summed and compared with the ground-truth total heat capacity. For all temperatures considered, the instantaneous normal mode approach gives the best prediction for LennardJones argon (within 1.1%), while for Stillinger-Weber silicon the temperature-dependent effective potential is best (within 0.7%). The Lennard-Jones argon mode heat capacities decrease with increasing frequency and are impacted by the effect of anharmonicity on the mode's self energy and its interactions with other modes. In Stillinger-Weber silicon, the acoustic mode heat capacities increase by up to 30% relative to the Dulong-Petit law, with these deviations driven by the interactions between modes. The proposed calculation framework will improve high-temperature thermal conductivity calculations, where the heat capacity is generally assumed to take on the harmonic value from the Dulong-Petit law.

X-ray micro computed tomography (X-mu CT) of bulk powder bed fusion-laser beam (PBF-LB) Ti-6Al-4V samples shows that, within the optimal process window-where lack-of-fusion and keyhole porosity are minimized-higher laser power reduces the number density of powder-entrapped pores when hatch spacing, layer thickness, and laser spot size remain fixed. To gain insight into this observation, the X-mu CT measurements of powder-entrapped pores are combined with a computational model to simulate pore trajectories in the PBF-LB melt pool. More than 100,000 independent pore trajectories are simulated at two different combinations of laser power and scanning velocity, where the forces acting on the pores are quantified using melt pool temperatures, pressures, and fluid flow velocities from multi-physics simulations. The model is then used to predict the pore size distributions in bulk samples fabricated within the optimal process window at 150 W, 700 mm/s and 370 W, 1200 mm/s. At both laser power settings, the total number density of pores predicted by the model is within one order of magnitude of the experimental values. The model suggests that the differences in the pore size distributions measured with X-mu CT are caused by differences in melt pool overlap (i.e., remelting). Using the model, a process map is constructed to predict porosity as a function of hatch spacing and layer thickness, suggesting that the number density of powder-entrapped pores can vary by two orders of magnitude within the optimal process window. This result suggests that the elimination of powder-entrapped pores poses an obstacle to increasing build rates by increasing the hatch spacing and layer thickness. While previous investigations of pore evolution during PBF-LB focused on experimental approaches, this work will enable the development of model-driven processing strategies to promote pore elimination.

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% Y2O3 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 × 1020 m−3. The largest mean diameter (72 nm) is observed at 200 W and 200 mm/s, with a number density of 1.5 × 1019 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.

Here, we describe the design features that lead to intrinsically thermally conductive polymers. Though polymers are conventionally assumed to be thermal insulators (<0.3 W m(-1) K-1), significant efforts by the thermal transport community have shown that polymers can be intrinsically thermally conductive (>1.0 W m(-1) K-1). However, these findings have not yet driven comprehensive synthetic efforts to expose how different macromolecular features impact thermal conductivity. Preliminary theoretical and experimental investigations have revealed that high k polymers can be realized by enhancing the alignment, crystallinity, and intermolecular interactions. While a holistic mechanistic framework does not yet exist for thermal transport in polymeric materials, contemporary literature suggests that phonon-like heat carriers may be operative in macromolecules that meet the abovementioned criteria. In this review, we offer a perspective on how high thermal conductivity polymers can be systematically engineered from this understanding. Reports for several classes of macromolecules, including linear polymers, network polymers, liquid-crystalline polymers, and two-dimensional polymers substantiate the design principles we propose. Throughout this work, we offer opportunities for continued fundamental and technological development of polymers with high thermal conductivity.

Traditional understanding of the thermal boundary resistance (TBR) across solid-solid interfaces posits that the vibrational densities of states overlap between materials dictates interfacial energy transport, with phonon scattering occurring at the interface. Using atomistic simulations, we show a mechanism for control of TBR; point defects near an interface can lead to both short- and midrange disorder, accelerating the conversion of vibrational energy between bulk and interfacial modes, ultimately reducing the TBR. We experimentally demonstrate this reduction through ion irradiation of gallium nitride and subsequently measuring the TBR across Al/GaN interfaces.

The pyrite phase of sodium superoxide, NaO2, is studied using equilibrium molecular dynamics simulations and lattice dynamics calculations to understand the impacts of static disorder and dynamic disorder on its thermal conductivity. Three structural regimes are observed based on the rotational dynamics and orientations of O2− ions. At low temperatures, where the O2− ions librate and the system is fully ordered, thermal conductivity exhibits a crystal-like temperature dependence, decreasing with increasing temperature. As temperature increases, the static disorder regime emerges, where the O2− ions transition between different orientations on a time scale larger than the librational period. In this regime, the thermal conductivity continues to decrease and then becomes temperature independent. At higher temperatures, where the O2− ions freely rotate, the system is dynamically disordered and the thermal conductivity is temperature independent, as in an amorphous solid. Using instantaneous normal mode analysis and Allen–Feldman theory, 80% of the thermal conductivity in the dynamic disorder regime is attributed to diffusons, vibrational modes that are non-propagating and non-localized. When increasing the lattice constant at a constant temperature, transitions from librations to static disorder to dynamic disorder are also observed, with the thermal conductivity decreasing monotonically. The presented methodology can be applied to other crystals with rotational degrees of freedom, offering strategies for the design of thermal conductivity switches that are responsive to external stimuli.

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.

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.

Feature extraction and a neural network model are applied to predict defect types and concentrations in experimental anatase TiO2 samples. A dataset of TiO2 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 and non-negative matrix factorization) and non-linear methods (autoencoder and convolutional neural network). The extracted features are used as inputs to a neural network that maps feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting defect concentrations based on experimentally measured TiO2 PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting defect concentrations. This model incorporates physical interpretability and predictability of material structures, enabling a more efficient characterization process with scattering data.

Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

The impact of rapid thermal annealing (1000 °C for 1 min) on the thermal transport properties of amorphous alumina (a-Al2O3) thin films grown by atomic layer deposition on β−Ga2O3 and amorphous silica (a-SiO2) substrates is determined using frequency-domain thermoreflectance measurements. The annealing more than doubles the a-Al2O3 thermal conductivity for both substrates (1.54 ± 0.13 to 3.14 ± 0.27 W m−1 K−1 for β−Ga2O3 and 1.60 ± 0.14 to 3.87 ± 0.33 W m−1 K−1 for a-SiO2) while keeping the film amorphous. The thermal conductivity increase is attributed to partial recrystallization and off-gassing of embedded impurities. Annealing halves the thermal boundary resistance of the a-Al2O3/a-SiO2 interface (10.5 ± 1.0 to 4.47 ± 0.42 m2 K GW−1), which is attributed to compositional mixing and structural reorganization that are enabled by the elastic matching of these two materials. The thermal boundary resistance of the a-Al2O3/β−Ga2O3 interface is not affected by annealing due to the elastic mismatch. Reducing the thermal resistance of a-Al2O3 dielectric films and adjacent interfaces by annealing will promote lateral heat spreading adjacent to hot spots and improve device longevity.

Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivity k in MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm−3), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m−1 K−1), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m−1 K−1), the structures of which we describe in additional detail.

With the objective of understanding 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 at 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 a 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, and we discuss their benefits and shortcomings. Assessing the potential application of molecular junctions as thermal diodes, 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 with Ag. Anharmonicity is built into the system within the molecular force-field. We find that, while the temperature profile strongly varies (compared with 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.

Thermal transport in metal-organic frameworks (MOFs) is an essential but frequently overlooked property. Among the small number of existing studies on thermal transport in MOFs, even fewer have considered explicitly the influence of defects. However, defects naturally exist in MOF crystals and are known to influence many of their material properties. In this work, we investigate the influence of both randomly and symmetrically distributed defects on the thermal conductivity of the MOF UiO-66. Two types of defects were examined: missing linker and missing cluster defects. For symmetrically distributed (i.e., spatially correlated) defects, we considered three experimentally resolved defect nanodomains of UiO-66 with underlying topologies of bcu, reo, and scu. We observed that both randomly distributed missing linker and missing cluster defects typically decrease thermal conductivity, as expected. However, we found that the spatial arrangement of defects can significantly impact thermal conductivity. In particular, the spatially correlated missing linker defect nanodomain (bcu topology) displayed an intriguing anisotropy, with the thermal conductivity along a particular direction being higher than that of the defect-free UiO-66. We attribute this unusual defect-induced increase in thermal conductivity to the removal of the linkers perpendicular to the primary direction of heat transport. These perpendicular linkers act as phonon scattering sources such that removing them increases thermal conductivity in that direction. Moreover, we also observed an increase in phonon group velocity, which might also contribute to the unusual increase. Overall, we show that structural defects could be an additional lever to tune the thermal conductivity of MOFs.