A map of single-phase high-entropy alloys

Abstract

High-entropy alloys have exhibited unusual materials properties. The stability of equimolar single-phase solid solution of five or more elements is supposedly rare and identifying the existence of such alloys has been challenging because of the vast chemical space of possible combinations. Herein, based on high-throughput density-functional theory calculations, we construct a chemical map of single-phase equimolar high-entropy alloys by investigating over 658,000 equimolar quinary alloys through a binary regular solid-solution model. We identify 30,201 potential single-phase equimolar alloys (5% of the possible combinations) forming mainly in body-centered cubic structures. We unveil the chemistries that are likely to form high-entropy alloys, and identify the complex interplay among mixing enthalpy, intermetallics formation, and melting point that drives the formation of these solid solutions. We demonstrate the power of our method by predicting the existence of two new high-entropy alloys, i.e. the body-centered cubic AlCoMnNiV and the face-centered cubic CoFeMnNiZn, which are successfully synthesized.

Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

“…Also, in recent years, high entropy alloys (HEAs) [55] have been proposed for many applications due to their promising properties, including the resistance to irradiation. In the specific case of HEAs, high-throughput computations and active learning methods can be combined to predict new stable compositions with tailored properties [56][57][58], which are methods that heavily rely on the availability of HPC. In any case, given the timelines of the ITER and IFMIF-DONES (International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source) experiments, research on fusion materials will be definitely guided by theoretical calculations using multiscale and exascale-oriented methods that will accelerate the future arrival of commercial fusion reactors.…”

Effects of Segregation on the Catalytic Properties of AgAuCuPdPt High-Entropy Alloy for CO Reduction Reaction

“…33 This alloy forms a stable FCC solid solution. 42 Furthermore, it contains elements that both weakly (Ag and Au) and strongly (Pd and Pt) bind the CO molecule together with Cu which is the only element that produces appreciable quantities of reduced products. Thus, in the sense of 'catalyst design by periodic table interpolation' proposed by Jacobsen et al, 43 this alloy contains the right elemental combination.…”

“…AgAuCuPdPt containing HEA was recently shown to be an active catalyst for electrochemical CO 2 reduction . This alloy forms a stable FCC solid solution . Furthermore, it contains elements that both weakly (Ag and Au) and strongly (Pd and Pt) bind the CO molecule together with Cu which is the only element that produces appreciable quantities of reduced products.…”

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

“…Therefore, removing Sn and screening E ̂f,M2 for (Al,Li,Mg,Zn) compositions, we can reduce the minimum E ̂h,M2 by ∼0.04 eV/atom, which remains high but marginally improves the potential for synthesizability from a thermodynamic perspective. In the future, FCC training databases with more elements can be developed 40 to enable accurate predictions of more alloying strategies or altogether different composition spaces that can identify candidate low-density high-entropy alloys with low or zero E h . The excellent stability and reversible hydrogen absorption of Pd have led to its establishment as a benchmark material in the hydrogen storage community.…”

“…Meanwhile, the clear limitation of this approach (and that of CE) occurs if lattice distortions are so great that there is no longer a unique mapping from the DFT-relaxed structure to its ideal lattice configuration, in which case one would likely resort to atomistic potentials and simulations for phase diagram predictions. We envision the buildup of large-scale FCC, BCC, HCP, etc., databases, 40 where the generalizability and flexibility of GNN models can be used for accurate predictions across large compositional spaces without explicitly accounting for, for example, the sparsity of the cluster functions in CE. We envision this to impact not only structural alloy and hydride design as demonstrated in this work but also the expansive realm of applications in which high-entropy materials are being investigated today.…”