General-purpose machine-learned potential for 16 elemental metals and their alloys.

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices.

Controlling Plasmonic Catalysis via Strong Coupling with Electromagnetic Resonators.

Plasmonic excitations decay within femtoseconds, leaving nonthermal (often referred to as "hot") charge carriers behind that can be injected into molecular structures to trigger chemical reactions that are otherwise out of reach─a process known as plasmonic catalysis. In this Letter, we demonstrate that strong coupling between resonator structures and plasmonic nanoparticles can be used to control the spectral overlap between the plasmonic excitation energy and the charge injection energy into nearby molecules. Our atomistic description couples real-time density-functional theory self-consistently to an electromagnetic resonator structure via the radiation-reaction potential.

Investigating the missing-wedge problem in small-angle X-ray scattering tensor tomography across real and reciprocal space.

Small-angle-scattering tensor tomography is a technique for studying anisotropic nanostructures of millimetre-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal space map can be probed.

Direct, Indirect, and Self-Trapped Excitons in CsAgBiBr.

CsAgBiBr exhibits promising photovoltaic and light-emitting properties, making it a candidate for next-generation solar cells and LED technologies. Additionally, it serves as a model system within the family of halide double perovskites, offering insights into a broader class of materials. Here, we study various possible excited states of this material to understand its absorption and emission properties.

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes.

Tailoring Hot-Carrier Distributions of Plasmonic Nanostructures through Surface Alloying.

Alloyed metal nanoparticles are a promising platform for plasmonically enabled hot-carrier generation, which can be used to drive photochemical reactions. Although the non-plasmonic component in these systems has been investigated for its potential to enhance catalytic activity, its capacity to affect the photochemical process favorably has been underexplored by comparison. Here, we study the impact of surface alloy species and concentration on hot-carrier generation in Ag nanoparticles.

Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics.

Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations.

Quantitative Predictions of the Thermal Conductivity in Transition Metal Dichalcogenides: Impact of Point Defects in MoS and WS Monolayers.

Transition metal dichalcogenides are investigated for various applications at the nanoscale because of their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the same time, these materials often contain relatively large amounts of point defects.

Tuning the Through-Plane Lattice Thermal Conductivity in van der Waals Structures through Rotational (Dis)ordering.

It has recently been demonstrated that MoS with irregular interlayer rotations can achieve an extreme anisotropy in the lattice thermal conductivity (LTC), which is, for example, of interest for applications in waste heat management in integrated circuits. Here, we show by atomic-scale simulations based on machine-learned potentials that this principle extends to other two-dimensional materials, including C and BN. In all three materials, introducing drives the through-plane LTC to the glass limit, while the in-plane LTC remains almost unchanged compared to those of the ideal bulk materials.

Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures.

The development of small-angle scattering tensor tomography has enabled the study of anisotropic nanostructures in a volume-resolved manner. It is of great value to have reconstruction methods that can handle many different nanostructural symmetries. For such a method to be employed by researchers from a wide range of backgrounds, it is crucial that its reliance on prior knowledge about the system is minimized, and that it is robust under various conditions.

Hot-Carrier Transfer across a Nanoparticle-Molecule Junction: The Importance of Orbital Hybridization and Level Alignment.

While direct hot-carrier transfer can increase photocatalytic activity, it is difficult to discern experimentally and competes with several other mechanisms. To shed light on these aspects, here, we model from first-principles hot-carrier generation across the interface between plasmonic nanoparticles and a CO molecule. The hot-electron transfer probability depends nonmonotonically on the nanoparticle-molecule distance and can be effective at long distances, even before a strong chemical bond can form; hot-hole transfer on the other hand is limited to shorter distances.

Ultrastrong Coupling of a Single Molecule to a Plasmonic Nanocavity: A First-Principles Study.

Ultrastrong coupling (USC) is a distinct regime of light-matter interaction in which the coupling strength is comparable to the resonance energy of the cavity or emitter. In the USC regime, common approximations to quantum optical Hamiltonians, such as the rotating wave approximation, break down as the ground state of the coupled system gains photonic character due to admixing of vacuum states with higher excited states, leading to ground-state energy changes. USC is usually achieved by collective coherent coupling of many quantum emitters to a single mode cavity, whereas USC with a single molecule remains challenging.

Investigating the Chemical Ordering in Quaternary Clathrate BaAlGaGe.

Recently, there has been an increased interest in quaternary clathrate systems as promising thermoelectric materials. Because of their increased complexity, however, the chemical ordering in the host framework of quaternary clathrates has not yet been comprehensively analyzed. Here, we have synthesized a prototypical quaternary type-I clathrate BaAlGaGe by Czochralski and flux methods, and we employed a combination of X-ray and neutron diffraction along with atomic scale simulations to investigate chemical ordering in this material.

Extremely anisotropic van der Waals thermal conductors.

The densification of integrated circuits requires thermal management strategies and high thermal conductivity materials. Recent innovations include the development of materials with thermal conduction anisotropy, which can remove hotspots along the fast-axis direction and provide thermal insulation along the slow axis. However, most artificially engineered thermal conductors have anisotropy ratios much smaller than those seen in naturally anisotropic materials.

Vitrification of octonary perylene mixtures with ultralow fragility.

Strong glass formers with a low fragility are highly sought-after because of the technological importance of vitrification. In the case of organic molecules and polymers, the lowest fragility values have been reported for single-component materials. Here, we establish that mixing of organic molecules can result in a marked reduction in fragility.

Relating atomic energy, radius and electronegativity through compression.

Trends in atomic properties are well-established tools for guiding the analysis and discovery of materials. Here, we show how compression can reveal a long sought-after connection between two central chemical concepts - van-der-Waals (vdW) radii and electronegativity - and how these relate to the driving forces behind chemical and physical transformations.

Dipolar coupling of nanoparticle-molecule assemblies: An efficient approach for studying strong coupling.

Strong light-matter interactions facilitate not only emerging applications in quantum and non-linear optics but also modifications of properties of materials. In particular, the latter possibility has spurred the development of advanced theoretical techniques that can accurately capture both quantum optical and quantum chemical degrees of freedom. These methods are, however, computationally very demanding, which limits their application range.

Surface Functionalization of 2D MXenes: Trends in Distribution, Composition, and Electronic Properties.

Using a multiscale computational scheme, we study the trends in distribution and composition of the surface functional groups -O, -OH, and -F on two-dimensional (2D) transition metal carbides and nitrides (MXenes). We consider TiN, TiN, NbC, NbC, TiC, and TiC to explore MXenes with different chemistry and different number of atomic layers. Using a combination of cluster expansion, Monte Carlo, and density functional theory methods, we study the distribution and composition of functional groups at experimentally relevant conditions.

To Every Rule There is an Exception: A Rational Extension of Loewenstein's Rule.

Loewenstein's rule, which states that Al-O-Al motifs are energetically unstable, is fundamental to the understanding and design of zeolites. Here, using a combination of electronic structure calculations and lattice models, we show under which circumstances this rule becomes invalid and how it can be rationally extended using the chabasite framework for demonstration.

Tunable Phases of Moiré Excitons in van der Waals Heterostructures.

Stacking monolayers of transition metal dichalcogenides into a heterostructure with a finite twist-angle gives rise to artificial moiré superlattices with a tunable periodicity. As a consequence, excitons experience a periodic potential, which can be exploited to tailor optoelectronic properties of these materials. Whereas recent experimental studies have confirmed twist-angle-dependent optical spectra, the microscopic origin of moiré exciton resonances has not been fully clarified yet.

Non-Bonded Radii of the Atoms Under Compression.

We present quantum mechanical estimates for non-bonded, van der Waals-like, radii of 93 atoms in a pressure range from 0 to 300 gigapascal. Trends in radii are largely maintained under pressure, but atoms also change place in their relative size ordering. Multiple isobaric contractions of radii are predicted and are explained by pressure-induced changes to the electronic ground state configurations of the atoms.