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Thermal transport across solid interfaces is of great importance for applications like power electronics. In this work, we perform non-equilibrium molecular dynamics simulations to study the effect of light atoms on the thermal transport across SiC/GaN interfaces, where light atoms refer to substitutional or interstitial defect atoms lighter than those in the pristine lattice. Various light atom doping features, such as the light atom concentration, mass of the light atom, and skin depth of the doped region, have been investigated. It is found that substituting Ga atoms in the GaN lattice with lighter atoms ( e.g. boron atoms) with 50% concentration near the interface can increase the thermal boundary conductance (TBC) by up to 50%. If light atoms are introduced interstitially, a similar increase in TBC is observed. Spectral analysis of interfacial heat transfer reveals that the enhanced TBC can be attributed to the stronger coupling of mid- and high-frequency phonons after introducing light atoms. We have also further included quantum correction, which reduces the amount of enhancement, but it still exists. These results may provide a route to improve TBC across solid interfaces as light atoms can be introduced during material growth. 

The optimization and application of new functional materials depends critically on our ability to manipulate the charge carrier density. Despite predictions of good n-type thermoelectric performance in the quaternary telluride diamond-like semiconductors ( e.g. Cu 2 HgGeTe 4 ), our prior experimental survey indicates that the materials exhibit degenerate p-type carrier densities (>10 20 h + cm −3 ) and resist extrinsic n-type doping. In this work, we apply the technique of phase boundary mapping to the Cu 2 HgGeTe 4 system. We begin by creating the quaternary phase diagram through a mixture of literature meta-analysis and experimental synthesis, discovering a new material (Hg 2 GeTe 4 ) in the process. We subsequently find that Hg 2 GeTe 4 and Cu 2 HgGeTe 4 share a full solid solution. An unusual affinity for Cu Hg and Hg Cu formation within Cu 2 HgGeTe 4 leads to a relatively complex phase diagram, rich with off-stoichiometry. Through subsequent probing of the fourteen pertinent composition-invariant points formed by the single-phase region, we achieve carrier density control ranging from degenerate (>10 21 h + cm −3 ) to non-degenerate (<10 17 h + cm −3 ) via manipulation of native defect formation. Furthermore, this work extends the concept of phase boundary mapping into the realm of solid solutions and clearly demonstrates the efficacy of the technique as a powerful experimental tool within complex systems. 

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across bothp- andn-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.