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Voronoi diagrams are widely used to model disperse systems such as foams, powders, polycrystals and atoms in the classical limit. Voronoi tessellations partition the continuous phase into compartments, or cells, that encompass all space closer to the assigning dispersed object than any other in the system. To account for heterogeneity in object size, weights are applied to avoid unphysical partitioning across non-contacting objects. Power and additive weighting are the most common weighting schemes, wherein power is more computationally tractable but additive weighting correlates more directly with size. In general, the two schemes produce distinct spatial decompositions for any non-monodisperse system. To calibrate the divergent volumetric metrics from the two schemes, and to gain physical insight into their divergence, we compared power and additively weighted Voronoi diagrams of polydisperse ensembles representing physically relevant ranges of polydispersity, density, and overlap. When tested against experimental distributions of gas foams, the results related their divergent power and additively weighted decompositions to the polydispersity of their particle size distributions. Geometric analysis of the Voronoi cells implicated the subpopulation of small objects as the primary contributors to the divergence through their preferential assignment of larger, aspherical power cells relative to their additively weighted counterparts. 

Modeling L-edge spectra at X-ray wavelengths requires consideration of spin–orbit splitting of the 2p orbitals. We introduce a low-cost tool to compute core-level spectra that combines a spin–orbit mean-field description of the Breit–Pauli Hamiltonian with nonrelativistic excited states computed using the semi-empirical density-functional theory configuration-interaction singles (DFT/CIS) method, within the state-interaction approach. Our version of DFT/CIS was introduced recently for K-edge spectra and includes a semi-empirical correction to the core orbital energies, significantly reducing ad hoc shifts that are typically required when time-dependent (TD-)DFT is applied to core-level excitations. In combination with the core/valence separation approximation and spin–orbit couplings, the DFT/CIS method affords semiquantitative L-edge spectra at CIS cost. Spin–orbit coupling has a qualitative effect on the spectra, as demonstrated for a variety of 3d transition metal systems and main-group compounds. The use of different active orbital spaces helps to facilitate spectral assignments. We find that spin–orbit splitting has a negligible effect on M-edge spectra for 3d transition metal species. 

Exact probabilistic inference is a requirement for many applications of probabilistic programming languages (PPLs) such as in high-consequence settings or verification. However, designing and implementing a PPL with scalable high-performance exact inference is difficult: exact inference engines, much like SAT solvers, are intricate low-level programs that are hard to implement. Due to this implementation challenge, PPLs that support scalable exact inference are restrictive and lack many features of general-purpose languages. This paper presents Roulette, the first discrete probabilistic programming language that combines high-performance exact inference with general-purpose language features. Roulette supports a significant subset of Racket, including data structures, first-class functions, surely-terminating recursion, mutable state, modules, and macros, along with probabilistic features such as finitely supported discrete random variables, conditioning, and top-level inference. The key insight is that there is a close connection between exact probabilistic inference and the symbolic evaluation strategy of Rosette. Building on this connection, Roulette generalizes and extends the Rosette solver-aided programming system to reason about probabilistic rather than symbolic quantities. We prove Roulette sound by generalizing a proof of correctness for Rosette to handle probabilities, and demonstrate its scalability and expressivity on a number of examples. 

ABSTRACT Molecular tools are increasingly being used to survey the presence of biodiversity and their interactions within ecosystems. Indirect methods, like environmental DNA (eDNA) and invertebrate‐derived DNA (iDNA), are dependent on sequence databases with accurate and sufficient taxonomic representation. These methods are increasingly being used in regions and habitats where direct detection or observations can be difficult for a variety of reasons. Madagascar is a biodiversity hotspot with a high proportion of endemic species, many of which are threatened or endangered. Here we describe a new resource, VoronaGasyCodes, a curated database of newly published genetic sequences from Malagasy birds. Our database is currently populated with six mitochondrial genes or DNA barcodes for 142 species including 70% of the birds endemic to the island and will be periodically updated as new data become available. We demonstrate the utility of our database with an iDNA study of leech blood meals where we successfully identified 77% of the hosts to species. These types of resources for characterising biodiversity are critical for insights into species distribution, discovery of new taxa, novel ecological connections and advancing conservation and restoration measures. 

The scalable and facile preparation of single-atom catalysts remains a critical challenge. Here, we introduce Diluted Atomic Layer Deposition (DALD), a unique approach for synthesizing supported metal catalysts with precisely tunable loadings. Unlike conventional metal deposition by ALD which uses pure metal precursors, DALD employs a diluted precursor mixture, combining organometallic precursors with the corresponding free ligand in controlled ratios. The method enables precise control over metal loadings, allowing the synthesis of structures ranging from nanoparticles to isolated single atoms, as exemplified by Ir, Rh, and Pt on high-surface-area γ-Al2O3. With its inherent simplicity and exceptional efficiency in metal precursor utilization, DALD represents a highly scalable strategy, unlocking opportunities for integrating single-atom catalysts into industrial processes. 

Abstract Laser cooling is used to produce ultracold atoms and molecules for quantum science and precision measurement applications. Molecules are more challenging to cool than atoms due to their vibrational and rotational internal degrees of freedom. Molecular rotations lead to the use of type-II transitions ( $F\text{⩾}{F}^{\prime }$) for magneto-optical trapping (MOT). When typical red detuned light frequencies are applied to these transitions, sub-Doppler heating is induced, resulting in higher temperatures and larger molecular cloud sizes than realized with the type-I MOTs most often used with atoms. To improve type-II MOTs, Jarviset al(2018Phys. Rev. Lett.120083201) proposed a blue-detuned MOT to be applied after initial cooling and capture with a red-detuned MOT. This was successfully implemented (Burauet al2023Phys. Rev. Lett.130193401; Jorapuret al2024Phys. Rev. Lett.132163403; Liet al2024Phys. Rev. Lett.132233402), realizing colder and denser molecular samples. Very recently, Hallaset al(2024 arXiv:2404.03636) demonstrated a blue-detuned MOT with a ‘1+2’ configuration that resulted in even stronger compression of the molecular cloud. Here, we describe and characterize theoretically the conveyor-belt mechanism that underlies this observed enhanced compression. We perform numerical simulations of the conveyor-belt mechanism using both stochastic Schrödinger equation and optical Bloch equation approaches. We investigate the conveyor-belt MOT characteristics in relation to laser parameters,g-factors and the structure of the molecule, and find that conveyor-belt trapping should be applicable to a wide range of laser-coolable molecules.