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The compositional space of high-entropy-alloy nanoparticles (HEA NPs) significantly expands the diversity of the materials library. Every atom in HEA NPs has a different elemental coordination environment, which requires knowledge of the local electronic structure at an atomic level. However, such structure has not been disclosed experimentally or theoretically. We synthesized HEA NPs composed of all eight noble-metal-group elements (NM-HEA) for the first time. Their electronic structure was revealed by hard X-ray photoelectron spectroscopy and density function theory calculations with NP models. The NM-HEA NPs have a lower degeneracy in energy level compared with the monometallic NPs, which is a common feature of HEA NPs. The local density of states (LDOS) of every surface atom was first revealed. Some atoms of the same constituent element in HEA NPs have different LDOS profiles, whereas atoms of other elements have similar LDOS profiles. In other words, one atom in HEA loses its elemental identity and it may be possible to create an ideal LDOS by adjusting the neighboring atoms. The tendency of the electronic structure change was shown by supervised learning. The NM-HEA NPs showed 10.8-times higher intrinsic activity for hydrogen evolution reaction than commercial Pt/C, which is one of the best catalysts.

There is significant unexplored potential when it comes to combining different elements. As such, computational science is looking into how combinations can be explored within cyberspace, enabling theoretical work to be conducted at impressive speeds. Professor Michihisa Koyama, Research Initiative for Supra-Materials, Shinshu University, Japan, is working to predict the catalytic activity of combined elements by employing an approach that involves a digital transformation of materials science. He and his team are developing cyber technologies to explore the infinite material space of nanoparticles, which involves the use of first-principles calculations based on quantum mechanics, as well as real system calculations. In their nanoparticle investigations, the researchers divide the real system of nanoparticles into three: super atoms, clusters and nanoparticles. Koyama and the team have been successful in their use of experimental science to synthesise nanoparticles and predict catalytic activity. The researchers believe that by 2030, they will have material development examples driven by their work.

Solid-solution alloy nanoparticles (NPs) comprising Pd and Ru, which are immiscible in the bulk state, have been synthesised and show excellent catalytic performance. To date, most studies have evaluated the stability of alloy NPs at 0 K only. Because the thermodynamic stability of Pd-Ru alloy NPs may differ from that of the alloy in the bulk state, the stable configuration of the NPs must be evaluated under a finite temperature. Such stability evaluations are critical for developing the durable NPs as catalysts. Therefore, the thermodynamic stability of Pd-Ru alloy NPs was analysed using density functional theory (DFT), supervised learning (SL), and Wang-Landau sampling. We calculated the excess energy of Pd-Ru alloy NPs, which depends on their composition, structure, NP size, adatom type, and defects, and applied SL to all models. The excess energies of the Pd-Ru alloy NPs expressed by structural information, such as the surface-to-volume ratio, correlated with those calculated using DFT. Wang-Landau sampling based on the energy estimated by SL gave the thermodynamic stability of Pd-Ru alloy NPs with a stable configuration under a finite temperature. The solid-solution atomic configuration was subdivided into partially mixed configurations in the surface layer or in the core of the NPs, which is different from the bulk state. The partially mixed configuration was determined by the overall composition and surface properties. The findings from the combined method could contribute to a better understanding of the alloy-NP stability and their application in catalysis.

Silicon monoxide is a potentially viable anode material for high-performance lithium-ion batteries (LIBs). However, a low initial coulombic efficiency and large volume expansion limit its commercial application. Pre-lithiation is an efficient solution, but is expensive because of limited "pre-lithiation" sources. In this work, we theoretically investigated a novel multiple pre-doping SiO system (Li-NaMg-SiO). By comparing its lithiation behavior to that of the traditional Li-doping system (Li-SiO), we revealed the different doping effects during lithiation. Similar to the traditional Li-doping system, the insertion of Na and Mg disintegrates the Si-O host matrix to form Na-O and Mg-O bonds and active Si clusters. At the end of lithiation, the O-Li coordination number (CN) tends to saturate at CN