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Accurate quantum mechanical benchmarks modeling ligand interactions with protein pockets are key in drug design. Here, such a large dimers dataset is created, analyzed, and probed with many methods

Γ and K valleys in twisted transition metal dichalcogenides have emerged as highly tunable knobs for accessing different correlated electronic states in solid-state devices. Here, the authors tune a Mott-Hubbard state to a charge-transfer insulator state in twisted double-bilayer WSe₂.

Van der Waals interactions have a large influence on phenomena that occur at short-length scales. Gobre et al.demonstrate that van der Waals interactions in low-dimensional materials act at very large distances, and can significantly influence the self-assembly of nanostructured systems.

Here the authors demonstrate an artificial-intelligence based approach to identify catalytic materials features that correlate with mechanisms that trigger, facilitate, or hinder CO2 catalytic reactions.

Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

By comparing CsSrBr3 and CsPbBr3 with close structural similarity but the latter lacking lone-pair electrons on the octahedral metal, the authors reveal the similar vibrational anharmonicities in the two perovskites, which disentangles the electronic properties and vibrational anharmonicities.

Polarons are hybrid particles comprising a charged particle coupled to lattice vibrations. Here, the authors identify a hole-based polaron of intermediate coupling strength in zinc oxide using infrared reflection–absorption spectroscopy and first-principles-based electronic structure theory calculations.

Scanning quantum dot microscopy, based on the use of a single molecule attached to the tip of the cantilever of an atomic force microscope, is shown to provide quantitative maps of surface potential distribution with atomic resolution.

The description of van der Waals interactions should often account for coupling with pervasive electric fields, but this effect has been omitted in atomistic simulations. Here, the authors develop a model to study the effects of external charge on long-range van der Waals interactions.

Here, a combined experiment-theory framework based on different nano-imaging techniques and first-principle calculations is used to analyse the shapes of moiré patterns in twisted van der Waals structures, enabling an accurate description of the coupling between the atomically thin layers.

Achieving high selectivity towards the formation of a single type of multi-carbon product from CO₂ electroreduction is difficult. Here Wu and colleagues show that the valence state of Cu can be tuned by functionalization of the catalyst surface with organic salts, boosting selectivity towards ethylene.

Pose estimation in combination with an anatomically constrained model allows inferring skeletal kinematics in rodents.

Twisted van der Waals systems are known to host flat electronic bands, originating from moire potential. Here, the authors predict from purely geometric considerations a new type of nearly dispersionless bands in twisted bilayer MoS₂, resulting from destructive interference between effective lattice hopping matrix elements.

Dissociative reactions in the solid state are prone to sample damage. Now, improved sample handling and measurement conditions enable the study of the dissociative reaction of a model triatomic system in the solid state on ultrafast timescales, revealing the significant impact of lattice coordination on the reaction pathway.

Attractive, non-covalent interactions between aromatic rings—termedπ−πstacking—is common in chemistry but difficult to model. Here the authors report a quantum-mechanical model to show the importance of collective charge fluctuations for understanding pi-stacked supramolecular systems.

Studies of twisted bilayer transition metal dichalcogenides have so far focused only on those containing group-VI metals. Here, the authors predict that twisted bilayers of ZrS₂, with the group-IV metal Zr, form an emergent moiré Kagome lattice with a uniquely strong spin-orbit coupling, leading to quantum-anomalous-Hall and fractional-Chern-insulating states.

Glycosyl cations are key intermediates in glycosylation reactions, but their structure has remained elusive due to their transient nature. Here, the authors perform an in-depth structural analysis and report that C2-participating protective groups induce acetoxonium cations with distinct ring conformations.

Simultaneous accurate and efficient prediction of molecular properties relies on combined quantum mechanics and machine learning approaches. Here the authors develop a flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations.

Current machine-learned force fields typically ignore electronic degrees of freedom. SpookyNet is a deep neural network that explicitly treats electronic degrees of freedom, closing an important remaining gap for models in quantum chemistry.

Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.

A findable, accessible, interoperable and reusable (FAIR) data infrastructure is discussed to turn the large amount of research data generated by the field of materials science into knowledge and value.

Functional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.

Carbonates are transported into the deep Earth by subduction of the oceanic lithosphere, but the stability fields of subducted carbonates as a function of pressure, temperature, and composition remain incompletely described. Here, the authors synthesize the anhydrous, mixed pyrocarbonate Ca3[C2O5]2[CO3] from Ca[CO3] and CO2 in a laser-heated diamond anvil cell at moderate pressure and elucidate its structural features.

In a newly fertilized egg, maternal and paternal chromosomes are enclosed in two separate pronuclei but the mechanisms in mammals for pronuclear movement are unclear. Here, the authors report that both F-actin and microtubule polymerization act in concern to drive inward movement of pronuclei towards the cell centre.

Plant diversity stabilizes grassland soil temperature by boosting soil organic carbon and increasing plant leaf area, according to an 18-year plant diversity experiment.

The present manuscript reports a Bayesian deep-learning approach for the automatic, robust classification of polycrystalline systems of both synthetic and experimental origin. The unsupervised analysis of the internal neural-network representations reveals physically understandable patterns.

Harasimov et al. show that, in human and porcine oocytes, actin cables and microtubule loops move chromosomes into a cluster before spindle assembly to ensure fast and complete chromosome capture in meiosis.

A miniaturized NMR-on-a-chip needle can be implanted into rodent brains and can measure blood flow and oxygenation changes in vivo in a small volume at an unprecedentedly high temporal resolution of a few milliseconds.

Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.

Powerful computer simulations have resolved the mechanism for the nanoscale assembly of the ‘hut’-like clusters that form after a few layers of atoms have been deposited on certain solid surfaces.

A computational workflow centered on probabilistic machine learning is developed to reconstruct the energy dispersion from photoemission band-mapping data. The workflow uncovers previously inaccessible momentum-space structural information at scale.

Luca Ghiringhelli introduces an AI toolkit that can be used with materials databases to discover new materials, or new properties of known materials.

The overwhelming number of possible high-entropy materials represents a big challenge for predicting their existence. Here, the authors introduce an entropy-forming-ability descriptor capturing the synthesizability of these systems, and apply the model to the discovery of new refractory metal carbides.

Functional connectivity and topic mapping of cortical networks reveal that an overlapping of functions exist within different thalamic nuclei, providing insight into the role of relay and higher-order nuclei with the cortex.