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Existing Moiré materials are mostly van der Waals heterostructures. Here the authors show that hydrogen-bond adaptability allows spontaneous formation of twisted bilayer ice at magic angles in 2D confinement, establishing a new class of Moiré materials.

The activity and stability of supported metal catalysts is in large part influenced by their interaction with the support. Now, neural network molecular dynamics simulations are combined with interpretable machine learning to reveal the governing factors of metal–support interactions for Pt nanoparticles on various oxide supports, identifying key features and proposing sinter-resistant supports.

To mitigate the lattice mismatch between GaN and Si in LEDs, AlN layers can be used as intermediary buffers. Here the authors identify a stable Si(320)/AlN interface with excellent thermal conductance using machine-learning-based structure predictions.

The reconstruction of rutile TiO2 (110) impacts its surface chemistry and catalytic properties. Here, the authors offer a detailed understanding of the asymmetric surface reconstruction of TiO2 (110)-(1×2) through a combination of STEM and DFT calculations.

It is commonly believed that pressure-induced crystallization in Ce-Al amorphous alloy is caused by Ce 4f orbital delocalization. Here, Wu et al. propose an alternative mechanism, whereby the crystallization is driven by a steric effect of dominant packing of cerium atoms at high pressure.

Binary ionic crystals, such as NaCl, are non-polar due to directionless ionic bonding interactions. Here, the authors show that polarity can be developed by using superalkali/superhalogen ions as building blocks, leading to ionic supersalts with ferroelectricity, ferroelasticity or even triferroicity.

The equilibrium structures and dynamics of a nanoscale system are regulated by a complex potential energy surface (PES), a key target of theoretical calculations but experimentally elusive. Here, the authors report the measurement of a key PES parameter for size-selected Au nanoclusters soft-landed on amorphous silicon nitride supports.

The polyhedral skeletal electron pair theory (PESPT), also known as Wade-Mingos’ rules, defines a relationship between skeletal bonding electron pairs and structure of clusters. Here the authors report the synthesis, structure and computational studies of planar C₂B₄R₄ carboranes that do not adhere to PESPT.

Polynitrogen compounds are of interest on a fundamental level and as potential high-energy-density materials. A crystalline solid that consists of two isomeric forms of N₈ molecules held together by weak van der Waals interactions has now been predicted to exist, and to be stable even at low pressures.

Caesium has so far not been found in oxidation states higher than +1, but quantum chemical calculations have now shown that, under high pressures, 5p inner shell electrons of caesium can participate in — and become the main components of — bonds. Caesium is predicted to form stable CsF_n molecules that resemble isoelectronic XeF_n.

Carbon–carbon single-bond rotation is a fundamental process in organic molecules. Here the authors use time-resolved X-ray liquidography to follow this motion in a haloalkane and its photodissociation product on an ultrafast timescale with atomic resolution.

Crystal polymorphism plays an important role in pharmaceuticals, agrisciences and other industries. Here the authors present an efficient and accurate crystal structure prediction method with large scale validation and blind test.

The presence of water adsorption sites plays a pivotal role in the suitability of metal-organic frameworks for atmospheric water harvesting. Here, the emergence of templated water motifs and their beneficial influence on adsorption are investigated.

LLMs can be augmented with tools to increase their capabilities. Here, authors have developed an artificial intelligence system called ChatMOF combining LLMs and specialised libraries and utilities to predict and generate metal-organic frameworks.

Icing plays an important role in various physical-chemical process. Here, the authors identify a critical value of surface energy parameter, above which two-dimensional and three-dimensional coexisting ice can form on the surface.

Previous theoretical interpretations of the Rydberg spectra of dimethylpiperazine (DMP) debated the existence of a localized minimum on the surface of the DMP+ cation. Here, the authors show a substantial influence of the Rydberg electron on the molecular structure, restoring the localized minimum.

Aberration-corrected scanning transmission electron microscopy, combined with dynamical multislice image simulations, can identify individual atoms in supported rhodium–iridium clusters and map their full structure.

Nitrogen can form a maximum of three shared electron-pair bonds to complete its octet, suggesting the maximum bond order of nitrogen is three. Here, the authors report a joint photoelectron spectroscopy and quantum chemical study, showing a quadruple bond between nitrogen and thorium in thorium nitride.

The lack of invertible and invariant crystal representations hinders the inverse design of crystals. Here the authors develop SLICES, an invertible and invariant representation, empowering property-driven inverse design of crystals using generative AI.

Understanding the phase behaviour of nanoconfined water is of importance in science & engineering. Here the authors use machine-learning force field molecular dynamics to report two new quasi-bilayer ice phases in the phase diagram of monolayer ices.

Biomacromolecule structures are vital for drug design. Here, the authors integrate one or two machine learning potentials in an ONIOM-based quantum refinement method, achieving reliable drug structures at an QM level in proteins with high efficiency.

Machine learning has not been strategically applied to design main-group catalysts. Here, the authors built an in-silico library of triarylboranes, which was used to identify reactive catalysts for functionalizing amino acids and peptides with H₂.

Normal mode analysis is a crucial step in structural biology, but is based on an expensive diagonalisation of the system’s Hessian. Here the authors present INCHING, a GPU-based approach to accelerate this task up to >250 times over current methods for macromolecular assemblies.

A computational model generates conformational ensembles of 28,058 intrinsically disordered proteins and regions (IDRs) in the human proteome and sheds light on the relationship between sequence, conformational properties and functions of IDRs.

The nucleation of calcium silicate hydrate is a crucial step in cement hydration, but is still a poorly understood process. Here the authors use atomistic simulations to study primary particles and their aggregation, revealing a potential C-S-H “basic building block”.

Shorter peptides are advantageous as protein binders, as they offer high-affinity binding to a diverse set of targets and lower manufacturing costs, but their flexibility and limited structural data make them difficult to design. Here, the authors introduce EvoBind2, a method for designing novel linear and cyclic peptide binders of varying lengths using only the sequence of a target protein, without prior knowledge of binding sites or predefined binder lengths.

Rare-earth and actinide complexes are critical for a wealth of clean-energy applications but Three dimensional (3D) structural generation and prediction for these organometallic systems remains challenging. Here, the authors propose a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes.

Complexes possessing coordination spheres that can accommodate nine ligand atoms typically display spherical distributions of these atoms. Here, the authors predict that M-centered [OB-B₇O₇-BO] adopts unusual heptagonal bipyramidal nonacoordination.

A lack of guiding principles limits the preparation of two-dimensional (2D) materials prepared by a solution-phase growth route. Now, a general qualitative model for 2D material growth is proposed and applied to fabricate more than 30 nanomaterials, allowing 2D growth to be controlled by only tuning the reaction concentration or temperature.

A generative deep learning model of molecular structure is combined with supervised deep learning models of molecular properties to achieve high-throughput (multi-)property-driven design of organic molecules.

The thermodynamic stability of atomically precise, liganded metal nanoclusters remains poorly understood. Here, the authors use first-principles calculations to derive a new theory that rationalizes the stability of these nanoclusters as a function of their composition and morphology.

Pressure causes profound changes in the properties of atoms and chemical bonding leading to unusual materials. Here, the authors investigate the Ca-C system and find that it becomes increasingly complex and develops a multitude of phases with various compositions and new structures at higher pressures.

A fully automated, high-throughput computational framework is presented to predict stable species in liquid solutions. This framework combines density functional theory with classical molecular dynamics simulations to compute the NMR chemical shifts.

Exploration of metastable phases of a given elemental composition is a data-intensive task. Here the authors integrate first-principles atomistic simulations with machine learning and high-performance computing to allow a rapid exploration of the metastable phases of carbon.

Predicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach.

The identification of active sites in cation exchanged-zeolites is computationally highly demanding. Here the authors employ a DFT-based computational framework to identify the energetically most favorable adsorption sites in any zeolite using a systematic high-throughput approach.

The structure of the electric double layer (EDL) has been a long-standing question since the 19th century. Here, the authors simulate EDL structures and highlight their importance in catalysis through comparison of atomic simulations and experiment.

The dynamic behaviour of low-nuclearity catalysts has major implications in their catalytic performance and is often overlooked. Here, it is shown how supported Rh catalysts undergo dynamic restructuring in response to gaseous products formed during steam reforming of methane.

Developing theoretical frameworks to predict new polymorphs is highly desirable. Here the authors present an ab initio based force-field approach for crystal structure prediction offering a dramatic computational speed-up over fully ab initio schemes.

Several rare earth carbides are superconducting at ambient pressure and low temperature. Here global structure searching predicts high-pressure metallic phases of yttrium dicarbide with phonon-mediated superconductivity and increased carbon network evolution from dimers to sheets with increasing pressure.

Ternary heterometallic clusters often display intriguing structures and bonding. Here the authors prepare four [Sn₂Sb₅]³⁻-based clusters stabilized by coordination of a transition metal ion; analysis of their electronic structure reveals that the resulting cluster displays globally aromatic or antiaromatic character depending on the transition metal ion.

Predicting the structure of unknown materials’ compositions represents a challenge for high-throughput computational approaches. Here the authors introduce a new stoichiometry-based machine learning approach for predicting the properties of inorganic materials from their elemental compositions.

Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.