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The one-electron oxidation of a hydrocarbon with an elongated C–C single bond provides direct evidence for a one-electron σ-bond between carbon atoms.

Hybrid density functionals are crucial for accurate materials calculations, yet their application is limited by the computational cost. Here, authors overcome this efficiency bottleneck through deep learning, enabling large-scale hybrid density functional calculations.

Using DFT simulations and STM measurements, the Si/Ag(001) surface reconstruction is revealed to be a Si-Ag surface alloy. Its atomic structure is composed of quasi-1D double-pentagon chains of silicon, electronically isolated by surface Ag atoms.

Li-rich oxides were found to slowly release considerable quantities of oxygen at varying states of charge. These observations point to the intrinsic instability of oxygen in partially charged oxides and highlight the need for degradation studies.

Conduction in solid-state electrolytes composed of monatomic ions is found to be analogous to the paddle-wheel mechanism in molecular solid electrolytes, facilitated by rotational motion of lone pair electrons, helping unify understanding of mechanisms.

Deep learning electronic structure calculations are generalized from the atomic-orbital basis to the plane-wave basis, resulting in higher accuracy, improved transferability and the capability to utilize existing electronic structure big data.

Grain boundaries strongly influence the properties of crystalline materials, but identifying their multiple phases remains difficult. Here, authors show a new tool for automated grain boundary structure prediction with an application to Ti.

The authors reveal that the incorporation of doping elements with varying electronic structures and ionic radii alters the atomic-scale configuration, thereby affecting the local energy barrier associated with polarization rotation.

Electronic simulations of large systems are computationally demanding. Here, authors develop DeePTB, a deep learning approach for efficient tight-binding calculations with ab initio accuracy, enabling million-atom simulations at finite temperatures.

Calculating electronic spectra of large systems is computationally challenging. Here, the authors combine exact short-time dynamics with approximate frequency space methods to capture narrow features embedded in a dense manifold of smaller peaks.

Kagome-lattice systems host a variety of condensed matter phenomena. Here, the authors use NMR spectroscopy to probe the orbital-selective modulations of the local density of states in the charge density wave phase of ScV₆Sn₆.

Structure relaxation is an iterative process particularly demanding in large-scale material discovery campaigns. Here, the authors realize a deep generative model able to relax material structures in a single step while estimating its accuracy.

Compositionally complex alloys have attracted significant attention recently, but the role of electronic correlations in these materials is unknown. Redka et al. study the CrMnFeCoNi alloy using a combination of experimental and theoretical techniques, revealing strong correlation effects far from the Fermi edge.

Semiconductor nanoplatelets emit light in narrow spectral ranges. Here, the authors establish a theoretical model showing this behavior is controlled by the inhomogeneities of the ligand layer on the nanoplatelet surface.

Machine learning potential has gained interest for replacing calculations in large systems. Here, authors propose an E(n) equivariant message passing neural network that uses high-order Cartesian tensors to predict properties with high accuracy

Diamondoids are a series of hydrogen-terminated nanometer-sized hydrocarbons that can be used to synthesize high-quality diamond crystals. Here, the authors use Monte Carlo simulations to study the potentials of different diamondoids in constructing diamond crystals with the assumption that the carbon skeletons keep intact, and find that higher diamondoid molecules are most suitable.

The performance of solid-state batteries is affected by stress responses of their complex microstructure to volume changes from Li⁺ intercalation. Here, authors present a chemo-mechanical model to study the impact of grain-level chemo-mechanics on the degradation of composite positive electrodes.

Materials fail by the three-dimensional (3D) propagation of cracks. The authors show that material disorder is essential for understanding a wide variety of experimental observations on 3D spatiotemporal failure dynamics and emerging structures.

Structural rules dictate that water molecules in bulk ice form four hydrogen bonds. Here, using atomistic simulations, the authors show that nanoconfined ice breaks these rules, and adopts a quasi-one-dimensional hydrogen-bonding network instead.

Understanding the silicon-oxygen system is crucial for various applications. Here, the authors present an interatomic potential covering a wide range of the Si-O configurational space and showcase applications to silica and Si-SiO2 interfaces.

Due to the complex, stochastic nature, fracture is a key challenge of artificial materials causing catastrophic failures. The authors overcame this by designing metamaterials that program damage and translate crack-resisting mechanisms

In solid-state lithium metal batteries, the crystallization of Li-ions deposited at interfaces remains unclear. Here, authors use molecular dynamics simulations to reveal lithium crystallization pathways and energy barriers, guiding improved interfacial engineering and accelerated crystal growth.

In this work, the authors implement a crystalline rank-2 chiral modes by employing non-Hermitian dynamics. They showed the momentum-resolved dynamics and non-Hermitian skin effect associated with the rank-2 chirality both theoretically and experimentally.

The Su-Schrieffer-Heeger (SSH) model is a prototypical model of topological states, initially proposed to describe spinless electrons on a one-dimensional (1D) dimerized lattice. Here, the authors realize a 2D SSH model in a rectangular lattice of silicon atoms on a silver substrate, observing gapped Dirac cones by angle-resolved photoemission spectroscopy.

Rod-like viruses show anomalously rapid diffusion in bio-tissue networks originated from the rotation-facilitated transportation but experimental investigation of the correlation of the rotational and translational dynamics is still lacking. Here, the authors use rod-like and spherical gold nanoparticles in Tetra-PEG gels and probe the hierarchical relaxation dynamics of the complex chemical system.

The mechanism by which two-dimensional materials remain stable at a finite temperature is still under debate. Now, numerical calculations suggest that rotational symmetry is crucial in suppressing anharmonic effects that lead to structural instability.

Anomalous lattice thermal conductivity increase with temperature in cubic GeTe is correlated with strengthening of second-nearest neighbor bonds at temperatures near that of the phase transition, enhancing our understanding of thermal transport in phase-change materials.

The effect of aliovalent doping on grain boundary is not yet fully understood at the atomic level. Here, the authors report grain boundary structural transformation in α-Al₂O₃ is induced by co-segregation of multiple dopants using atomic-resolution electron microscopy and theoretical calculations.

Level of atomic disorder in materials is critical to understanding the effect of local structure on materials properties. Here the authors present a workflow combining structure-aware graph neural networks and physics-inspired order parameter to characterize structural disorder on a per atom basis.

Understanding catalytic reaction pathways is key for optimizing catalyst. Here, the authors develop a generalizable and high-throughput reinforcement learning strategy for the study of chemical reactions, enabling efficient, accurate and autonomous prediction of reaction pathways.

Auger processes are at the core of electron emission in solid-state physics, however measuring the spectra of electrons emitted solely as a result of Auger transitions remains a challenge. Here, the authors measure the electron energy spectrum in graphene and observe the prominence of Auger-like processes in its valence band.

Studying the properties of dense plasmas is challenging due to strong interactions between electrons and ions, and numerical methods overcome this difficulty using a static thermostat. Here the authors predict a strong diffusive ion mode at low energy by including dissipative processes in the model.

Aqueous clusters are important in a variety of biological and materials systems. Here Willset al. develop a theoretical approach based on a group additivity relationship which allows the evaluation of a wide range of clusters without the need of cumbersome ab initiocalculations.

Ab initioatomistic modelling reveals that fast ion diffusion in super-ionic conductors is mediated by concerted migration of multiple ions. On the basis of this understanding, a universal strategy for designing fast ion conductors is proposed and demonstrated.

Pore structure plays an important role in dictating gas storage performance for nanoporous materials. Here, Smit and colleagues develop a topological approach to quantify pore structure similarity, and exploit the resulting descriptor to screen for materials that possess structural similarities with top-performers.

Metallopolyelectrolytes are versatile materials for various applications, utilizing dynamic, reversible electrostatic interactions. However, the knowledge gap between molecular-level chemistry and material properties persists. Here, the authors present a framework combining theoretical and experimental insights, highlighting the interplay of molecular parameters in governing material properties.

A machine learning framework is proposed to accurately predict electron–phonon coupling (EPC) strengths while reducing computational costs compared with first-principles methods. This approach facilitates EPC calculations with advanced functionals, allowing the accurate determination of real-world material properties such as carrier mobility and superconductivity.

The current work finds that ordinary table salt behaves unexpectedly. The chloride ions alone dominate the electronic states at both edges of its band gap although sodium ions are also present. This is important when NaCl is used as an insulator.