Computational screening of high-entropy alloy (HEA) catalysts as alternatives to the typical Cu electrocatalyst for CO2 reduction reaction (CO2RR) has been extensively focused on C1 products, but C 2 + products have received significantly less attention. This work optimized CuZnPdAgAu HEA catalyst composition for CO2RR to ethylene via density functional theory and supervised machine learning regression techniques. Candidates were identified from 106,045 HEA data for enthalpy of adsorption of *CO2, *H, *HOCCOH, and *C2H4 species, and the Gibbs free energy of *H. The electrocatalytic properties during the reaction were examined on the surface of the optimized HEA candidate - Cu 0.36 Zn 0.18 Pd 0.10 Ag 0.18 Au 0.18 benchmarked to Cu (111). The Pd site of such a candidate functions as the active site for the CO2 activation step. In terms of catalytic activity, it showed lower Gibbs free energy for the potential determining step - the *OCCOH formation step compared to that on the Cu (111). Insight into electronic properties demonstrated that the candidate reduces the uphill reaction energy for *HOCCOH production pathway due to increased electron density in the C-C bond, donated from two Cu sites. It is shown that the HEA catalyst candidate has the potential for CO2RR targeting ethylene, an alternative to a common Cu catalyst.

Considering charge transfer effects and the variability of the bonding between elements with different electronegativity opens up a deeper understanding of the electronic structure and as a result many of the properties in high entropy-related materials. This study investigates the importance of the diverse bonding and chemical environments when discussing multicomponent carbide materials. A combination of ab initio calculations and X-ray photoelectron spectroscopy (XPS) was used to investigate the electronic structure of multicomponent thin films based on the (HfNbTiVZr)C system. The charge transfer was quantified theoretically using relaxed and nonrelaxed multicomponent as well as binary carbide reference structures, employing a fixed sphere model. High-resolution XPS spectra from (HfNbTiVZr)C magnetron-sputtered thin films displayed core-level binding energy shifts and broadening effects as a result of the complex chemical environment. Charge transfer effects and a changed electronic structure in the multicomponent material, compared with the reference binary carbides, are observed both experimentally and in the density functional theory (DFT) simulations. The observed effects loosely follow electronegativity considerations, leading to a deviation from an ideal solid solution structure assuming nondistinguishable chemically equivalent environments.

The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of ab initio calculations. Recently, several pretrained universal machine learned interatomic potentials (UPMLIPs) have been published, i.e., potentials distributed with a single set of weights trained to target systems across a very wide range of chemistries and atomic arrangements. These potentials raise the hope of reducing the computational cost and methodological complexity of performing simulations compared to models that require for-purpose training. However, the application of these models needs critical evaluation to assess their usability across material types and properties. In this work, we investigate the application of the following UPMLIPs: MACE, CHGNET, and M3GNET to the context of alloy theory. We calculate the mixing enthalpies and volumes of 21 binary alloy systems and compare the results with DFT calculations to assess the performance of these potentials over different properties and types of materials. We find that the small relative energies necessary to correctly predict mixing energies are generally not reproduced by these methods with sufficient accuracy to describe correct mixing behaviors. However, the performance can be significantly improved by supplementing the training data with relevant training data. The potentials can also be used to partially accelerate these calculations by replacing the ab initio structural relaxation step.

The effects of B-site substitution (BMn, Fe, and Co) in La-based perovskite oxides (LPOs); LaMnO3, LaFeO3, LaCoO3, as bifunctional electrocatalysts during oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in metal-air batteries (MABs) under an alkaline electrolyte (pH = 13) are investigated using density functional theory (DFT). It is found that LaMnO3 exhibits higher ORR activity than others with ORR overpotential (eta ORR) of 0.57 V, but its OER activity is poor with OER overpotential (eta OER) of 1.12 V. The eta ORR (0.59 V) and eta OER (1.13 V) of LaMn0.75Fe0.25O3 closely resemble those of LaMnO3, suggesting that Fe substitution does not yield appreciable enhancements in activity. Fe substitution reduces the ORR and OER activity because the adsorption energies of intermediate species on Fe-substituted LPOs surfaces are too strong to obtain a potential determining step for ORR and OER. According to Sabatier's principle, the LaMn0.25Co0.75O3 demonstrates superior OER activity compared to the other composition, while ORR activity approximates that of LaMnO3, evidenced by eta ORR of 0.65 V and eta OER of 0.53 V. The Co-terminated LaMn0.25Co0.75O3 shows bifunctional activity higher than Mn/Co termination, indicating that Co is an active site for OER and Mn is a promoter for improved ORR activity. The effects of B-site substitution (BMn, Fe, and Co) on La-based perovskite oxides during oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are investigated via density functional theory. LaMn0.25Co0.75O3 as a promising bifunctional electrocatalyst exhibits ORR/OER overpotentials of 0.65 V/0.53 V due to the presence of Mn and Co promoting electron transfer. image

Density functional theory is used to compare the catalytic performance of PtPdRhFeCo(100) high entropy alloy (HEA) three-way catalyst (TWC) to the conventional Pt(100) in the NO reduction step during NH3 production that supplies to passive NH3-SCR. Stronger adsorption of NO on the HEA(100) surface is beneficial to capture NO. During adsorption, the catalyst surface acts as an electron donor while the adsorbate is the acceptor on both HEA(100) and Pt(100) systems. Herein, the reaction mechanism of NO reduction can be classified into two steps: 1) NO activation and 2) product formation. During NO activation, direct NO dissociation is the preferable pathway on both HEA(100) and Pt(100) surfaces with the same Ea, whereas HNO and NOH pathways on HEA(100) are suppressed. For NH3, N2, and N2O production on HEA(100) is found to be more difficult than on Pt(100). However, the thermodynamic driving force of all reactions on HEA(100) is more spontaneous than on Pt(100). Also, the rate-determining step on HEA(100) is found to be NH3 formation different from the Pt(100), while difficult H diffusion on HEA(100) is the key factor that reduces NH3 production.

High-entropy-alloy (HEA) catalysts have been used in many challenging electrocatalytic reactions, e.g., CO2 reduction reaction (CO2RR) due to their promising properties. For CO2RR catalysts, tuning metal compositions in Cu-based catalysts is one of the techniques to control the desired products. Thus, this work investigated the optimal composition of Cu-Mn-Ni-Zn HEA catalysts using high-throughput screening (HTS) for CO2RR targeting on two competing routes toward CH4 and CH3OH products. The screening protocol evaluates catalytic activity through adsorption energy (Eads) of *CO2, *CO, *COOH, and *H. At the same time, the selectivity is represented by Eads of *COH, *CH4, *CHO, and *CH3OH, using density functional theory (DFT) accelerated by machine learning techniques. The screening result from 11,920 data revealed 259 candidates for CH4-selective and 4,214 for CH3OH-selective catalysts. Interestingly, the Cu-Mn-Ni-Zn excellently prevented competitive hydrogen evolution reaction by up to 90%. Optimal composition for each route are Cu0.1Mn0.4Ni0.2Zn0.3 and Cu0.2Mn0.4- Ni0.1Zn0.3 in CH4-selective route and Cu0.3Mn0.3Ni0.2Zn0.2, Cu0.3Mn0.2Ni0.3Zn0.2, Cu0.3Mn0.2Ni0.2Zn0.3, and Cu0.2Mn0.3Ni0.3Zn0.2 in CH3OH-selective route. The optimal catalyst structure with high CO2RR activity in both routes was revealed to have the Mn atom as an active site, while Cu, Ni, and Zn as neighboring atoms. Hence, the Cu-Mn-Ni-Zn HEA catalyst is the promising electrocatalyst for CO2RR.

The representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree-based ensembles and multi-layer perceptron regression, to explore the alloying behavior of different elements in multi-component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition-metal ((Ti-Mo-V)(3)C-2 MXenes, a multi-component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition-metal ground-state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground-state structures is thoroughly discussed from various aspects.

Thermodynamic stability as well as structural, electronic, and elastic properties of boron-deficient AlB2-type tantalum diborides, which is designated as alpha-TaB2-x, due to the presence of vacancies at its boron sublattice are studied via first-principles calculations. The results reveal that alpha-TaB2-x, where 0.167 less than or similar to x less than or similar to 0.25, is thermodynamically stable even at absolute zero. On the other hand, the shear and Youngs moduli as well as the hardness of stable alpha-TaB2-x are predicted to be superior as compared to those of alpha-TaB2. The changes in the relative stability and also the elastic properties of alpha-TaB2-x with respect to those of alpha-TaB2 can be explained by the competitive effect between the decrease in the number of electrons filling in the antibonding states of alpha-TaB2 and the increase in the number of broken bonds around the vacancies, both induced by the increase in the concentration of boron vacancies. A good agreement between our calculated lattice parameters, elastic moduli and hardness of alpha-TaB2-x and the experimentally measured data of as-synthesized AlB2-type tantalum diborides with the claimed composition of TaB similar to 2, available in the literature, suggests that, instead of being a line compound with a stoichiometric composition of TaB2, AlB2-type tantalum diboride is readily boron-deficient, and its stable composition in equilibrium may be ranging at least from TaB similar to 1.833 to TaB similar to 1.75. Furthermore, the substitution of vacancies for boron atoms in alpha-TaB2 is responsible for destabilization of WB2-type tantalum diboride and orthorhombic Ta2B3, predicted in the previous theoretical studies to be thermodynamically stable in the Ta-B system, and it thus enables the interpretation of why the two compounds have never been realized in actual experiments.

Active-site models comprise miniature active sites on the host element, providing one of effective descriptors for screening high-entropy-alloy (HEA) catalysts using machine learning. This study investigates the impact of host elements on the electronic properties of active sites via density functional theory (DFT), where the active-site model is used in the HEA electrocatalysts. Also, the appropriate host element selection significantly affects the systems surface structures, electronic, and catalytic properties that adsorbate adsorption energy, d-band center, Bader charge, Zero-point energy, and entropy are used as accuracy verification parameters compared to the original surface. Ultimately, the novel guideline for active-site model construction is proposed using the simple example of PtPdFeCoNi high-entropy alloys. This investigation demonstrates that the host element selection is a crucial parameter to the active-site models, influencing the electronic structure and electrocatalytic properties.

Transition metal borides are known due to their attractive mechanical, electronic, refractive, and other properties. A new class of rhenium borides was identified by synchrotron single-crystal X-ray diffraction experiments in laser-heated diamond anvil cells between 26 and 75 GPa. Recoverable to ambient conditions, compounds rhenium triboride (ReB3) and rhenium tetraboride (ReB4) consist of close-packed single layers of rhenium atoms alternating with boron networks built from puckered hexagonal layers, which link short bonded (similar to 1.7 angstrom) axially oriented B-2 dumbbells. The short and incompressible Re-B and B-B bonds oriented along the hexagonal c-axis contribute to low axial compressibility comparable with the linear compressibility of diamond. Sub-millimeter samples of ReB3 and ReB4 were synthesized in a large-volume press at pressures as low as 33 GPa and used for material characterization. Crystals of both compounds are metallic and hard (Vickers hardness, H-V = 34(3) GPa). Geometrical, crystal-chemical, and theoretical analysis considerations suggest that potential ReBx compounds with x > 4 can be based on the same principle of structural organization as in ReB3 and ReB4 and possess similar mechanical and electronic properties.