Browse Articles

The new apicomplexan species Monocystis cantonensis from the earthworm Amynthas aspergillum retains a micropore but lacks an apicoplast

Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange–correlation functionals

Accurate prediction of a material’s melting temperature is critical for materials design and high-temperature applications. In this work, we investigate melting behavior across a deliberately selected set of elemental metals spanning systems where cohesive-energy trends suggest that PBE and PBEsol are expected to perform differently, as well as cases where their performance is ambiguous. Melting temperatures are computed using the two-phase coexistence (TPC) approach in conjunction with a machine-learned interatomic potential based on the moment tensor potential (MTP) framework, enabling large-scale simulations that minimize finite-size effects and ensure sufficient equilibration. The TPC-MTP results reveal a clear functional dependence in the predicted melting temperatures. PBE provides good agreement for several lighter elements, whereas PBEsol gives the best overall agreement across the full dataset. However, the element-resolved trends are not governed by cohesive energy alone, indicating that liquid-phase energetics, anharmonicity, and finite-temperature phase stability also contribute to the observed functional dependence. For intermediate and structurally complex systems, both functionals exhibit less systematic performance. Overall, this study provides a systematic assessment of functional-dependent melting temperature predictions, highlighting both the strengths and limitations of the combined TPC–MTP methodology and underscoring the need for carefully selected exchange–correlation treatments in high-accuracy melting-point simulations.

Serum per- and polyfluoroalkyl substances levels among young adults at risk for chronic kidney disease of unknown etiology in Nicaragua

Thermodynamics of quantum oscillators

In this work, we present a compact analytical approximation for the quantum partition function of systems composed of quantum oscillators. The proposed formula is general and applicable to an arbitrary number of oscillators described by a rather general class of potential energy functions (not necessarily polynomials). Starting from the exact path-integral expression of the partition function, we introduce a temperature-dependent Gaussian approximation for the high-temperature propagator and then invoke a principle of minimal sensitivity to minimize the error. This leads to a system of coupled nonlinear equations whose solution yields the optimal parameters of the Gaussian approximation. The resulting approximate partition function accurately reproduces thermodynamic quantities such as the free energy, average energy, and specific heat—even at zero temperature—with typical relative errors in the range of about 1%–5%. The accuracy deteriorates only moderately when the anharmonicity and coupling strengths are increased. We illustrate the performance of our analytical formula with numerical results for systems of up to ten coupled anharmonic oscillators. These results are compared with “exact” numerical results obtained via Hamiltonian diagonalization for small systems and path-integral Monte Carlo simulations for larger ones.

Accurate prediction of nitrogen fixation in cyanobacteria reveals the dynamic evolution driving high retention rate with mosaic distribution

Abstract Cyanobacteria are the only prokaryotic group capable of performing oxygenic photosynthesis and nitrogen fixation (catalyzed by extremely oxygen-sensitive nitrogenase) simultaneously. A systematic understanding of the phylogenetic distribution of nitrogen-fixing ability remains incomplete due to difficulties in accurately identifying genes essential for nitrogen fixation from genomic data, caused by occasional confusion with nitrogenase-like enzymes and fragmentation of nif genes in heterocystous species. In this study, we comprehensively evaluated the distribution of the minimal nitrogen fixation gene set ( nifHDKENB ) across 586 major cyanobacterial strains. Overall, 48% of the strains were predicted to be nitrogen-fixing with a mosaic distribution in non-heterocystous strains. Using the results as reference data, we evaluated nitrogen fixation across a broader range of cyanobacterial lineages, including uncultured strains and metagenomes. We predicted that approximately 20% of 2,718 strains are nitrogen fixers, revealing a high nif retention rate of nif genes in the Cyanobacteria comparing with other bacterial phyla. While most cyanobacteria carry Group I nif genes, we identified widely distributed lineages with Group II nif genes, some of which are associated with distinct CnfR proteins. We discuss the dynamic evolutionary history of nif genes in cyanobacteria.

Interaction balance theory

The classification of electrolytes and prediction of their properties is a fundamental challenge in electrolyte thermodynamics. Understanding the balance of ionic interactions is key to accurately describing solution behavior. We introduce the interaction balance theory, a framework that links microscopic ionic interactions to macroscopic activity coefficients enabling the decomposition of intermolecular interactions, systematic analysis of the sources of non-ideality, and a quantitative classification of electrolytes. Application to the sodium halides NaF, NaCl, NaBr, and NaI in water and non-aqueous solvents shows that the theory captures their distinct intermolecular behaviors, distinguishing the relative contributions of long-range Coulombic forces, short-range repulsions, and solvent-mediated interactions and correlating these decompositions with the salts’ solubility. Our results also show that the minimum in activity coefficient marks the end of a specific equilibrium between the cumulative short- and long-range forces, following the considered Ewald decomposition of the forces. This approach provides a clear, quantitative method to interpret experimental data, disentangle what essentially builds the non-ideality of systems, and to guide the development of thermodynamic models for electrolytes, highlighting which interactions dominate in different systems.

Hospital-level sepsis bundle compliance and its association with hospital performance in sepsis and septic shock in a nationwide cohort study

Topology-directed optimization of block copolymer architecture for self-assembly into spherical vesicles using machine learning

Amphiphilic block copolymers are a promising class of macromolecules used for creating new functional containers for delivering active agents into the cells. The diversity of these macromolecules' architectures allows for the selection of the necessary system parameters to generate the desired morphologies of these containers. Where the standard grid-search approach proves costly, optimization algorithms can directly search for the desired structure using a data-aware approach. The challenge here lies in the numerical characterization of such vesicles with a single objective function, that is, finding a functional capable of indicating the degree of proximity of the aggregate to the desired class of structures, for example, vesicles. This paper describes a novel pipeline for the topology-directed search for molecular parameters that enable the spontaneous formation of spherical vesicles in a system of amphiphilic comb–coil copolymers with variable architectural and solvent parameters within simulations. A key feature of the presented method is the use of a probabilistic classification model, trained on ideal structures, as a functional describing the “vesicularity” of the aggregate obtained in the point-based simulation. The classification of structures is based on topological data analysis, and, as shown, topological characteristics are sufficient to accurately distinguish typical structures self-assembling in solutions of amphiphilic macromolecules. Optimization of this probabilistic functional using a classic Bayesian optimization algorithm allows finding parameters consistent with spherical vesicle formation in a reasonable number of iterations. By design, the usage of this methodology is not restricted by the considered class of polymers and can be applied to other simulated macromolecular systems.

Valorization of agricultural hazelnut waste into high-surface-area activated carbon: sustainable methodology for accurate VOC capture and thermodynamic assessment

Large-scale molecular-dynamics simulations on heterogeneous state during decompression-induced transformation of short-range order in silica glass

Large-scale molecular-dynamics simulations with machine-learning interatomic potentials have shown that a nanometer-scale heterogeneous intermediate state emerges during the structural transformation of silica glass from a sixfold-coordinated state to a fourfold-coordinated state under decompression. The simulation results reproduce key experimental observations, including density changes and low-Q scattering intensity during decompression. The heterogeneity is explained by the distribution of the coordination number for silicon. The transformation proceeds via a relatively unstable fivefold-coordinated state, and its generation is followed by the formation of a fourfold-coordinated component that increases in proportion to the logarithm of time.

Data assets and corporate green innovation: implications for sustainable development

Prediction challenge: Cyclobutanone photochemistry

Multi-class performance evaluation of artificial intelligence based methods for PV fault detection from thermal images

Attosecond charge migration in glycine and N-methylacetamide following sudden ionization: A TD-DMRG study

Attosecond charge migration following sudden ionization probes ionic-state coherence and multielectronic correlation and, therefore, requires multireference real-time methods able to treat large active spaces. Here, we apply the time-dependent density matrix renormalization group (TD-DMRG) with the time-dependent variational principle (TDVP) to study the early time charge migration in gas-phase glycine and N-methylacetamide (NMA) molecules within the fixed-nuclei, purely electronic regime. Target ionic states were constructed with matrix-product-state-based multireference configuration interaction on complete active space self-consistent field orbitals, and active orbitals were selected from a state-averaged one-electron reduced density matrix (1-RDM), yielding final active spaces of glycine (21e, 18o) and NMA (19e, 17o). The analysis of local partial charges, real-space hole densities, fixed-orbital hole occupations, and the autocorrelation function shows that the selected ionization channels follow distinct early time electronic-motion mechanisms. In glycine, the 10a′, 11a′, and 14a′ channels sample three regimes: a correlation-driven inner-valence response with enhanced two-hole-one-particle satellite-state participation and multiorbital redistribution in 10a′; backbone-mediated charge redistribution in 11a′; and compact few-state terminal-group exchange in 14a′. The selected NMA 13a′ channel, used as a benchmark for peptide-bond charge migration, is governed mainly by one-hole mixing and gives a regular back-and-forth oscillation across the amide region. The results identify the initially ionized orbital and the configurational composition of the ionic states as key factors controlling early time charge migration. TD-DMRG/TDVP therefore offers a practical ab initio route for simulating post-ionization electronic wave packets in relatively large active spaces.

Robust geometric preprocessing reveals architecture-conditional spectral collapse in heterogeneous fleet anomaly detection

Identification of the glass transition temperature <i>T</i> <i>g</i> via selective dynamical slowing in bulk polymer melts using unsupervised machine learning

Determining the glass transition temperature Tg in materials science in general and for amorphous polymer systems in particular is a delicate matter due to the uncertainty in the definition of Tg and the complexity of the glass transition phenomenon itself. Machine learning (ML) provides powerful approaches for analyzing complex, high-dimensional data to reveal hidden patterns. Recently, we have applied an unsupervised ML technique to identify Tg of a polymer melt of weakly semiflexible bead-spring chains using the time evolvement of pairwise internal distances between monomers along chains, as input features. Here, we investigate the change of individual internal chain relaxation as the polymer melt transforms from the liquid to glassy state. The average overall relaxation remains unchanged and displays the usual temperature dependence. However, for some individual pair distances, scattered throughout the sample, relaxation is significantly delayed, which serves as a robust indicator of approaching the glass transition. Moreover, these changes and the first principle components are highly correlated. This is an evidence that the ML technique indeed captures a significant indicator of the approach of the glass transition. Typical experiments, which average over the whole sample, cannot identify such features.

Neutrophil-derived IL-4 induces BDNF expression in microglia within the infarct cortex and surrounding regions following cerebral ischemia

Minimizing propagated density errors of atomic core-electron for simultaneously accurate bandgaps and lattice constants in closed-shell copper semiconductors

Density functional theory struggles to accurately determine the electron density of atoms, whose error is inevitably encoded into the pseudopotential and propagated into solid-state calculations. However, little is known about how this affects accuracy nor how to remedy it. In this work, through a systematic study of the effect of Cu atomic density on bandgap and lattice constants of over 50 Cu-containing simple closed-shell semiconductors, we find that core-electron density can drastically affect nuclear attraction to valence electrons and subsequent charge distribution and energy position of Cu 3d electrons. The error can be eliminated at its source by employing modified Hartree–Fock pseudopotentials for the Cu core while retaining (semi-)local functionals for valence electrons. This real-space partitioning approach leads to simultaneous high accuracy in bandgap and lattice constants across the entire material class.

Ultrasound pretreatment of dentin enhances the adhesion of universal adhesives

<i>paces</i> : Parallelized application of co-evolving subspaces. A method for computing quantum dynamics on GPUs

An efficient method of solving the time-dependent Schrödinger equation for pure states is described: At each time step, a restricted subspace of the total Hilbert space is systematically and naturally constructed via the image of repeated applications of the Hamiltonian operator and the time evolution is computed exactly within the restricted subspace. The subspace is dynamically recomputed such that it co-evolves with the state vector. The method is built from the ground up as a parallel algorithm for graphics processing units and suited to Hamiltonians that are sparse in a given basis. We benchmark the method by comparing its results for a 1D Holstein model with previously published multiset-matrix-product state results and then apply the method to compute optical spectra and non-equilibrium dynamics of one-, two-, and three-dimensional model chromophore nanoaggregates.