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Abstract Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: CHfTa, CHfZr, CMoW, and CTaTi. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the CHfTa, CHfZr, and CTaTi systems, and in the case of the CMoW system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred (Mo_1−xW_x)C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT. 

Abstract The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics^1–3. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor⁴, most innovation has been slowly driven by experimental means^1–3. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries. 

Herein, we critically evaluate computational and experimental studies in the emerging field of high-entropy ultra-high-temperature ceramics. High-entropy ultra-high-temperature ceramics are candidates for use in extreme environments that include temperatures over 2,000°C, heat fluxes of hundreds of watts per square centimeter, or irradiation from neutrons with energies of several megaelectron volts. Computational studies have been used to predict the ability to synthesize stable high-entropy materials as well as the resulting properties but face challenges such as the number and complexity of unique bonding environments that are possible for these compositionally complex compounds. Experimental studies have synthesized and densified a large number of different high-entropy borides and carbides, but no systematic studies of composition-structure-property relationships have been completed. Overall, this emerging field presents a number of exciting research challenges and numerous opportunities for future studies. 

Abstract High-entropy ceramics are attracting significant interest due to their exceptional chemical stability and physical properties. While configurational entropy descriptors have been successfully implemented to predict their formation and even to discover new materials, the contribution of vibrations to their stability has been contentious. This work unravels the issue by computationally integrating disorder parameterization, phonon modeling, and thermodynamic characterization. Three recently synthesized carbides are used as a testbed: (HfNbTaTiV)C, (HfNbTaTiW)C, and (HfNbTaTiZr)C. It is found that vibrational contributions should not be neglected when precursors or decomposition products have different nearest-neighbor environments from the high-entropy carbide.