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Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and the Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. We validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications. 

Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as potential replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space using exclusively experimental approaches is impractical due to the considerable cost of the synthesis, processing, and testing of candidate alloys, particularly at operation-relevant temperatures. On the other hand, the lack of reasonably accurate predictive property models, especially for high-temperature properties, makes traditional Integrated Computational Materials Engineering (ICME) methods inadequate. In this paper, we address this challenge by combining machine-learning models, easy-to-implement physics-based models, and inexpensive proxy experimental data to develop robust and fast-acting models using the concept of Bayesian updating. The framework combines data from one of the most comprehensive databases on RHEAs (Borg et al., 2020) with one of the most widely used physics-based strength models for BCC-based RHEAs (Maresca and Curtin, 2020) into a compact predictive model that is significantly more accurate than the state-of-the-art. This model is cross-validated, tested for physics-informed extrapolation, and rigorously benchmarked against standard Gaussian process regressors (GPRs) in a toy Bayesian optimization problem. Such a model can be used as a tool within ICME frameworks to screen for RHEAs with superior high-temperature properties. The code associated with this work is available at: https://codeocean.com/capsule/7849853/tree/v2.