Carbon-based transition metal (TM) single-atom catalysts (SACs) have shown a great potential toward electrochemical water splitting and H2 production. Given that two-dimensional (2D) materials are widely exploited for sustainable energy...

MXenes have been widely used as substrates of hybrid electrocatalysts for water splitting due to their stability and metallic properties. However, tuning MXenes towards superb hydrogen/oxygen evolution reaction (HER/OER) activity has remained elusive. Using first‐principles calculations along with machine learning (ML) based descriptors, it is shown that late transition metal doping is able to significantly promote HER/OER activities. Both single‐atom adsorption onto a stable hollow site above the outer oxygen layer single‐atom catalyst 1 (SAC1), and single‐atom replacement at a sub‐surface metal layer (SAC2) are considered. An adsorbate evolving mechanism (AEM) is preferred for SAC1, while the increased M‐O bond covalency for SAC2 makes lattice oxygen mechanism (LOM) favored. It is found that a single Ni or Co atom embedded into MXenes provides a suitable number of electrons for optimal AEM and raises the O 2p band towards activated LOM. The stability and superb bifunctional catalytic capability of MXene combinations (Ni‐doped Sc3N2O2 and Ni‐doped Nb3C2O2) towards both HER and OER are demonstrated. The electronic and geometric descriptors used in the ML analysis work universally for classification of high‐performing HER/OER catalysts. This work provides a rational strategy for promoting bifunctional electrocatalytic activities based on low‐cost MXenes metals.

To tune single-atom catalysts (SACs) for effective nitrogen reduction reaction (NRR), we investigate various transition metals implanted on boron-arsenide (BAs), boron-phosphide (BP), and boron-antimony (BSb) using density functional theory (DFT). Interestingly, W-BAs shows high catalytic activity and excellent selectivity with an insignificant barrier of only 0.05 eV along the distal pathway and a surmountable kinetic barrier of 0.34 eV. The W-BSb and Mo-BSb exhibit high performances with limiting potentials of −0.19 and −0.34 V. The Bader-charge descriptor reveals that the charge transfers from substrate to *NNH in the first protonation step and from *NH3 to substrate in the last protonation step, circumventing a big hurdle in NRR by achieving negative free energy change of *NH2 to *NH3. Furthermore, machine learning (ML) descriptors are introduced to reduce computational cost. Our rational design meets the three critical prerequisites of chemisorbing N2 molecules, stabilizing *NNH, and destabilizing *NH2 adsorbates for high-efficiency NRR.

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Designing highly efficient bifunctional and multifunctional catalysts for hydrogen/oxygen evolution reaction (HER/OER) and oxygen reduction reaction (ORR) has attracted acute attention, toward the development of clean and renewable energy technologies....

Rechargeable sodium‐ion batteries (SIBs) are emerging as a viable alternative to lithium‐ion battery (LIB) technology, as their raw materials are economical, geographically abundant (unlike lithium), and less toxic. The matured LIB technology contributes significantly to digital civilization, from mobile electronic devices to zero electric‐vehicle emissions. However, with the increasing reliance on renewable energy sources and the anticipated integration of high‐energy‐density batteries into the grid, concerns have arisen regarding the sustainability of lithium due to its limited availability and consequent price escalations. In this context, SIBs have gained attention as a potential energy storage alternative, benefiting from the abundance of sodium and sharing electrochemical characteristics similar to LIBs. Furthermore, high‐entropy chemistry has emerged as a new paradigm, promising to enhance energy density and accelerate advancements in battery technology to meet the growing energy demands. This review uncovers the fundamentals, current progress, and the views on the future of SIB technologies, with a discussion focused on the design of novel materials. The crucial factors, such as morphology, crystal defects, and doping, that can tune electrochemistry, which should inspire young researchers in battery technology to identify and work on challenging research problems, are also reviewed.

The influence of cation mixing on the oxygen evolution reaction (OER) activity of a La x Sr1–x Co y Fe1–y O3 (LSCF) double perovskite is investigated using density functional theory (DFT) calculations. The O 2p band center (E 2p) has a good linear relation with the binding energy of the OER intermediate species when the chemical composition is varied by only the x or y value, but this relation is insufficient for describing the nonmonotonic behavior over the entire x and y ranges. Based on the projected density of states and wavefunction analysis, the minority spin d xy electrons of surface layer metal atoms are significant due to their stability, where the antibonding states between d xy and the lattice oxygen p become occupied when Co atoms with one d electron more than Fe are present. Thus, by additionally considering the d xy band center, a surface electronic descriptor (E 2p – 0.4E d xy ) excellently describes the binding energy of the OER intermediates and the stability against oxygen-vacancy formation, which also explains the enhanced OER stability and efficient Fe–Co mixing. Our study unveils the key mechanism of the excellent OER performance and high stability of previously reported LSCF materials as well as provides heterostructure engineering guidance for optimal surface electronic structures.

We apply on-the-fly machine learning potentials (MLPs) using the sparse Gaussian process regression (SGPR) algorithm for fast optimization of atomic structures. Great acceleration is achieved even in the context of a single local optimization. Although for finding the exact local minimum, due to limited accuracy of MLPs, switching to another algorithm may be needed. For random gold clusters, the forces are reduced to ∼0.1 eV/˚A within less than 10 first-principles (FP) calculations. Because of highly transferable MLPs, this algorithm is specially suitable for global optimization methods such as random or evolutionary structure searching or basin hopping. This is demonstrated by sequential optimization of random gold clusters for which, after only a few optimizations, FP calculations were rarely needed.