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1. Strength can be controlled by edge dislocations in refractory high-entropy alloys

   https://doi.org/10.1038/s41467-021-25807-w  

   Lee, Chanho; Maresca, Francesco; Feng, Rui; Chou, Yi; Ungar, T.; Widom, Michael; An, Ke; Poplawsky, Jonathan D.; Chou, Yi-Chia; Liaw, Peter K.; et al (December 2021, Nature Communications)

   Abstract Energy efficiency is motivating the search for new high-temperature (high-T) metals. Some new body-centered-cubic (BCC) random multicomponent “high-entropy alloys (HEAs)” based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known. Here we show, using integrated in-situ neutron-diffraction (ND), high-resolution transmission electron microscopy (HRTEM), and recent theory, that the high strength and strength retention of a NbTaTiV alloy and a high-strength/low-density CrMoNbV alloy are attributable to edge dislocations. This finding is surprising because plastic flows in BCC elemental metals and dilute alloys are generally controlled by screw dislocations. We use the insight and theory to perform a computationally-guided search over 10 7 BCC HEAs and identify over 10 6 possible ultra-strong high-T alloy compositions for future exploration. 

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2. Alloying effects on the transport properties of refractory high-entropy alloys

   https://doi.org/10.1016/j.actamat.2024.120032  

   Singh, Prashant; Acemi, Cafer; Kuchibhotla, Aditya; Vela, Brent; Sharma, Prince; Zhang, Weiwei; Mason, Paul; Balasubramanian, Ganesh; Karaman, Ibrahim; Arroyave, Raymundo; et al (September 2024, Acta Materialia)

   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. 

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3. A Novel Low-Activation VCrFeTaxWx (x = 0.1, 0.2, 0.3, 0.4, and 1) High-Entropy Alloys with Excellent Heat-Softening Resistance

   https://doi.org/10.3390/e20120951  

   Zhang, Weiran; Liaw, Peter; Zhang, Yong (December 2018, Entropy)

   The microstructure, Vickers hardness, and compressive properties of novel low-activation VCrFeTaxWx (x = 0.1, 0.2, 0.3, 0.4, and 1) high-entropy alloys (HEAs) were studied. The alloys were fabricated by vacuum-arc melting and the characteristics of these alloys were explored. The microstructures of all the alloys exhibited a typical morphology of dendritic and eutectic structures. The VCrFeTa0.1W0.1 and VCrFeTa0.2W0.2 alloys are essentially single phase, consisting of a disordered body-centered-cubic (BCC) phase, whereas the VCrFeTa0.2W0.2 alloy contains fine, nanoscale precipitates distributed in the BCC matrix. The lattice parameters and compositions of the identified phases were investigated. The alloys have Vickers hardness values ranging from 546 HV0.2 to 1135 HV0.2 with the x ranging from 0.1 to 1, respectively. The VCrFeTa0.1W0.1 and VCrFeTa0.2W0.2 alloys exhibit compressive yield strengths of 1341 MPa and 1742 MPa, with compressive plastic strains of 42.2% and 35.7%, respectively. VCrFeTa0.1W0.1 and VCrFeTa0.2W0.2 alloys have excellent hardness after annealing for 25 h at 600–1000 °C, and presented compressive yield strength exceeding 1000 MPa with excellent heat-softening resistance at 600–800 °C. By applying the HEA criteria, Ta and W additions into the VCrFeTaW are proposed as a family of candidate materials for fusion reactors and high-temperature structural applications. 

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4. A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni

   https://doi.org/10.1038/s41524-023-01021-8  

   Vazquez, G.; Chakravarty, S.; Gurrola, R.; Arróyave, R. (December 2023, npj Computational Materials)

   Abstract High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks. 

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5. Predicting vacancy formation energies in refractory non-dilute random alloys using improved graph-based machine learning models trained on density functional theory data

   https://doi.org/10.1063/5.0304829  

   Fani, Mahshad; Akinloye, Oluwatimilehin; Nathani, Anvesh; Mubassira, Subah; Ghamarian, Iman; Xu, Shuozhi (January 2026, Journal of Applied Physics)

   This study employs four machine learning (ML) models—random forest (RF), XGBoost, graph convolutional networks (GCN), and graph attention networks (GAT)—to predict vacancy formation energies (VFEs) in refractory non-dilute random alloys including pure metals, binary, ternary, quaternary, and quinary systems based on Mo, Nb, Ta, V, and W. Training data are generated from density functional theory calculations. Among all elements, W has the highest VFE on average (2.866 → 3.517 eV from pure metal to ternary), while Ta is the only element whose VFE rises monotonically (2.841 → 3.262 eV from pure metal to quinary). In tree-based tabular models (RF, XGBoost), each chemical composition is encoded as a five-dimensional vector of elemental fractions on the Mo–Nb–Ta–V–W basis, augmented with a categorical indicator of the specific element in a certain alloy for which the vacancy is created, and features are standardized to balance input scales during training. In graph models (GCN, GAT), each composition is represented as a small graph whose nodes carry per-element descriptors (fraction, atomic number, and Magpie features), while edges summarize simple pairwise chemistry (such as fraction interactions and differences in atomic number, electronegativity, covalent radius, and Mendeleev number). All four models achieve high accuracy in predicting VFEs across alloy orders, with graph-based approaches capturing chemistry-aware trends beyond fixed tabular descriptors. Among the four, GCN attains the highest accuracy with R2=0.972, root mean squared error =0.051 eV, and mean absolute error =0.042 eV, outperforming GAT and the tabular baselines. These results underscore the promise of graph-based ML for rapid, reliable prediction of VFEs in the refractory alloy design. 

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