The δ phase of plutonium with the fcc structure exhibits an unusual negative thermal expansion (NTE) over its narrow temperature range of stability, 593–736 K. An accurate description of the anomalous high-temperature volume effect of plutonium goes...
The paper presents an analytical review of theoretical methods for modeling functional nanostructures. The main evolutionary changes in the approaches of quantum-mechanical modeling are described. The foundations of the first-principal theory are con...
Based on the literature data on the structure of liquid Pu, the pair contribution to the EAM potential of liquid Pu is calculated and the embedding potential is determined using the dependence of density on temperature. The properties of liquid Pu ar...
The Snoek relaxation profiles for ternary Niobium–Vanadium–Oxygen systems were analyzed by an embedded-cell model of statistical mechanics treatment. The relaxation characteristic and broadening mechanism were systematically discussed and...
Phosphorus atoms in steels accumulate at grain boundaries via thermal and irradiation effects and induce grain boundary embrittlement, which is experimentally confirmed by an increase in the ductile-brittle transition temperature. Quantitative predic...
Due to the high oxygen evolution reaction (OER) activity, stability, and abundance of NiOx materials, they are found to be promising catalysts, competitive with expensive metal oxides such as IrO2 and RuO2. From a theoretical point of view, studies r...
The spontaneous deposition of platinum (
A variational heteroencoder based on recurrent neural networks, trained with SMILES linear notations of molecular structures, was used to derive the following atomic descriptors: delta latent space vectors (DLSVs) obtained from the original SMILES of...
Interatomic interaction potentials are compared using a molecular dynamics modeling method to choose the simplest, but most effective, model to describe the interaction of copper nanoparticles and graphene flakes. Three potentials are considered: (1)...
The work described in this paper develops a formal method for modeling and exhaustive verification of mutual exclusion algorithms. The process is based on timed automata and the Uppaal model checker. The technique was successfully applied to several...
The structural and energetic properties of small silver clusters Ag
Lamport’s Bakery algorithm is a well-known, simple, and elegant solution to the mutual exclusion problem for N ≥ 2 concurrent/parallel processes. However, the algorithm generates an unbounded number of tickets, even when only 2 processes are...
The oxidation mechanism of CO on W-embedded graphene was investigated by M06-2X density functional theory. Two models of tungsten atom embedded in single and double vacancy (W-SV and W-DV) graphene sheets were considered. It was found that over W-SV-...
Nanocomposites composed of Cu and Mo were investigated by means of molecular dynamics (MD) simulations to study the incoherent interface between Cu and Mo. In order to select an appropriate potential capable of accurately describing the Cu-Mo system,...
Molecular dynamics simulations of body-centered cubic (bcc) iron thin films with crack defects were carried out by adopting methods of EAM (Embedded Atom Method) potential, spin/exchange potential and spin/neel potential. In this article, the effects...
The formation of intermetallic compounds (IMC) at the Al/Fe interface determined the mechanical property of steel-aluminum welded joint. To understand the interfacial microstructure evolution and relate diffusion mechanism of atoms cross the Al/Fe in...
The effect of Debye plasma on the
Molecular property prediction is an important direction in computer-aided drug design. In this paper, to fully explore the information from SMILE stings and graph data of molecules, we combined the SALSTM and GAT methods in order to mine the feature...
Collisionless fragmentation of a non-rotating Ni19 cluster is studied using constant-energy molecular dynamics computer simulations. The cluster is modelled by an embedded atom model (EAM) energy surface. Distribution of the channel-specific fragment...
Polaron quasiparticles are formed when a mobile impurity is coupled to the elementary excitations of a many-particle background. In the field of ultracold atoms, the study of the associated impurity problem has attracted a growing interest over the l...
We combine the charge equilibration (QEq) method with the modified embedded atom model (MEAM) to describe a series of intermetallic GaxPdy compounds at near DFT accuracy. Apart from structure, energetics and elastic properties, a particular focus is...
The new ternary amalgam CsNa
Defects in crystalline solids play a crucial role in determining properties of materials at the nano, meso- and macroscales, such as the coalescence of vacancies at the nanoscale to form voids and prismatic dislocation loops or diffusion and segregat...
We report density functional electronic structure calculations to monitor the change in the surface characteristics of the Si (100)-2x1 surface after fluorination followed by interaction with water. Embedded finite silicon clusters are used to model...
The energy eigenvalues of the ground state helium atom and lowest two excited states corresponding to the configurations 1s2s embedded in the plasma environment using Hulthén, Debye–Hückel and exponential cosine screened Coulomb mod...
Graphene is considered to be one of the most promising reinforcement phases for nanocomposites due to its unique two-dimensional planar structure with excellent mechanical properties. After the design of origami, the 2D material will obtain a negativ...
The thermodynamic and transport properties of weakly non-ideal, high-density partially ionized hydrogen plasma are investigated, accounting for quantum effects due to the change in the energy spectrum of atomic hydrogen when the electron–proton...
The conformational properties of carbohydrates can contribute to protein structure directly through covalent conjugation in the cases of glycoproteins and proteoglycans and indirectly in the case of transmembrane proteins embedded in glycolipid-conta...
This study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databa...
The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs....
The surfaces of polymer and interfaces between polymer and inorganic particles are of particular importance for the properties of polymers and composites. However, the determination of the properties of surfaces and interfaces poses many challenges d...
γ-Aminobutyric acid (GABA) is the most important inhibitory neurotransmitter in the central nervous system, and a deficiency of GABA is associated with serious neurological disorders. Due to its low lipophilicity, there has been an intensive se...
This work investigates the difference in the fragmentation characteristics between the microscopic and macroscopic scales under hypervelocity impact, with the simulations of Molecular Dynamics (MD) and Smoothed Particle Hydrodynamics (SPH) method. Un...
Crystals of NaSn2F5 were obtained from an aqueous solution. Their morphology and habitus were studied via scanning electron microscopy and X-ray phase diffraction analysis. The crystals obtained have a long prismatic shape with a very large aspect ra...
The interest in single-atom catalysts (SACs) is increasing, as these materials have the ultimate level of catalyst utilization, while novel reactions where SACs are used are constantly being discovered. However, to properly understand SACs and to fur...
Embedding energetically stable single metal atoms in the surface of Pt nanocatalysts exposed to varied temperature (T) and hydrogen pressure (P) could open up new possibilities in selective and dynamical engineering of alloyed Pt catalysts, particula...
In the binding process between proteins and ligand molecules, water molecules play a pivotal role by forming hydrogen bonds that enable proteins and ligand molecules to bind more strongly. However, current methodologies for predicting binding affinit...
We have previously reported the synthesis, in vitro and in silico activities of new GABA analogues as inhibitors of the GABA-AT enzyme from Pseudomonas fluorescens, where the nitrogen atom at the γ-position is embedded in heterocyclic scaffolds...
Large language models (LLMs) frequently produce text that is fluent yet factually inconsistent with source documents. Detecting such inconsistency remains challenging, particularly when errors involve subtle entity substitutions, temporal distortions...
This paper describes the application of bonded magnetic abrasive powders (MAPs) in the magnetic abrasive finishing (MAF) process. In order to improve the poor finishing performance and short service life of MAPs in polishing super-hard materials, a d...
The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this w...
RNA has emerged as a critical drug target, and accurate prediction of its binding affinity with small molecules is essential for the design and screening of RNA-targeted therapeutics. Although current deep learning methods have achieved progress in p...
Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and mor...
Voids in face-centered cubic (fcc) metals are commonly assumed to form via the aggregation of vacancies; however, the mechanisms of vacancy clustering and diffusion are not fully understood. In this study, we use computational modeling to provide a d...
Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including ling...
We present a modified embedded atom method (MEAM) semi-empirical force-field model for the Ti1−xAlxN (0 ≤ x ≤ 1) alloy system. The MEAM parameters, determined via an adaptive simulated-annealing (ASA) minimization scheme, optimize the mod...
The formation of CaSi2 polycrystalline structures under the postgrowth electron irradiation of epitaxial CaF2/Si(111) films with embedded thin Si layers was studied. The dependence on the electron exposure time was investigated for two types of struc...
We investigate the phenomenon of quantum interference in spontaneous emission pathways for a quantum emitter embedded in a two-dimensional photonic crystal composed of a square lattice of dielectric cylindrical rods. Using a V-type three-level system...
The mechanical properties of copper alloys containing various ratios of zinc are evaluated using molecular dynamics (MD) simulations to determine the impact of the different zinc concentrations. The modified embedded atom method (MEAM) parameters for...
Conductive indium-tin oxide (ITO) and non-conductive glass substrates were successfully modified with embedded gold nanoparticles (AuNPs) formed by controlled thermal annealing at 550 °C for 8 h in a preselected oven. The authors characterized th...
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