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High-purity and superfine high-entropy metal diboride powders, namely (Hf 0.2 Zr 0.2 Ta 0.2 Nb 0.2 Ti 0.2 )B 2 , were successfully synthesized via a facile borothermal reduction method at 1973 K for the first time. The as-synthesized powders with an average particle size of ~ 310 nm had a single-crystalline hexagonal structure of metal diborides and simultaneously possessed high compositional uniformity from nanoscale to microscale. In addition, their formation mechanisms were well interpreted by analyzing the thermodynamics of the possible chemical reactions based on the first principles calculations. This work will open up a new research field on the synthesis of high-entropy metal diboride powders.

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To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

The formation possibility of (Hf 0.2 Zr 0.2 Ta 0.2 Nb 0.2 Ti 0.2 )C high-entropy ceramic (HHC-1) was first analyzed by the first-principles calculations, and then, it was successfully fabricated by hot-pressing sintering technique at 2073 K under a pressure of 30 MPa. The first-principles calculation results showed that the mixing enthalpy and mixing entropy of HHC-1 were −0.869 ± 0.290 kJ/mol and 0.805R, respectively. The experimental results showed that the as-prepared HHC-1 not only had an interesting single rock-salt crystal structure of metal carbides but also possessed high compositional uniformity from nanoscale to microscale. By taking advantage of these unique features, it exhibited extremely high nanohardness of 40.6 ± 0.6 GPa and elastic modulus in the range from 514 ± 10 to 522 ± 10 GPa and relatively high electrical resistivity of 91 ± 1.3 μΩ·cm, which could be due to the presence of solid solution effects. K E Y W O R D S electrical resistivity, first-principles calculations, high-entropy ceramics, mechanical properties, metal carbides

In this work, a novel (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)(N0.5C0.5) high‐entropy nitride‐carbide (HENC‐1) with multi‐cationic and ‐anionic sublattice structure was reported and their thermophysical and mechanical properties were studied for the first time. The results of the first‐principles calculations showed that HENC‐1 had the highest mixing entropy of 1.151R, which resulted in the lowest Gibbs free energy above 600 K among HENC‐1, (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)N high‐entropy nitrides (HEN‐1), and (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)C high‐entropy carbides (HEC‐1). In this case, HENC‐1 samples were successfully fabricated by hot‐pressing sintering technique at the lowest temperature (1773 K) among HENC‐1, HEN‐1 and HEC‐1 samples. The as‐fabricated HENC‐1 samples showed a single rock‐salt structure of metal nitride‐carbides and high compositional uniformity. Meanwhile, they exhibited high microhardness of 19.5 ± 0.3 GPa at an applied load of 9.8 N and nanohardness of 33.4 ± 0.5 GPa and simultaneously possessed a high bulk modulus of 258 GPa, Young's modulus of 429 GPa, shear modulus of 176 GPa, and elastic modulus of 572 ± 7 GPa. Their hardness and modulus are the highest among HENC‐1, HEN‐1 and HEC‐1 samples, which could be attributed to the presence of mass disorder and lattice distortion from the multi‐anionic sublattice structure and small grain in HENC‐1 samples. In addition, the thermal conductivity of HENC‐1 samples was significantly lower than the average value from the “rule of mixture” between HEC‐1 and HEN‐1 samples in the range of 300‐800 K, which was due to the presence of lattice distortion from the multi‐anionic sublattice structure in HENC‐1 samples.