Types Of Atoms Research Articles

EasyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms.

The dynamics of metal centers are challenging to describe due to the vast variety of ligands, metals, and coordination spheres, hampering the existence of general databases of transferable force field parameters for classical molecular dynamics simulations. Here, we present easyPARM, a Python-based tool that can calculate force field parameters for a wide range of metal complexes from routine frequency calculations with electronic structure methods. The approach is based on a unique labeling strategy, in which each ligand atom that coordinates the metal receives a unique atom type. This design prevents parameter shortage, labeling duplication, and the necessity to post-process output files, even for very complicated coordination spheres, whose parametrization process remain automatic. The program requires the Cartesian Hessian matrix, the geometry xyz file, and the atomic charges to provide reliable force-field parameters extensively benchmarked against density functional theory dynamics in both the gas and condensed phases. The procedure allows the classical description of metal complexes at a low computational cost with an accuracy as good as the quality of the Hessian matrix obtained by quantum chemistry methods. easyPARM v2.00 reads vibrational frequencies and charges in Gaussian (version 09 or 16) or ORCA (version 5 or 6) format and provides refined force-field parameters in Amber format. These can be directly used in Amber and NAMD molecular dynamics engines or converted to other formats. The tool is available free of charge in the GitHub platform (https://github.com/Abdelazim-Abdelgawwad/easyPARM.git).

Analyzing Oxygen Atom Distribution in FDA-Approved Drugs to Enhance Drug Discovery Strategies.

Despite advancements in molecular design rules and understanding biochemical processes, the field of drug design and discovery seeks to minimize the number and duration of synthesis-testing cycles to convert lead compounds into drug candidates. A promising strategy involves gaining insightful understanding of key heteroatoms such as oxygen and nitrogen. This work presents a comprehensive analysis of oxygen atoms in approved drugs, aiming to streamline drug design and discovery efforts. The study examines the frequency, distribution, prevalence, and diversity of oxygen atoms in a dataset of 2049 small molecules approved by the FDA and other agencies. The analysis focuses on various types of oxygen atoms, including sp3, sp2-hybridized, ring, and nonring. In general, existence of sp3-O slightly outperforms sp2-O, which is associated with balancing various factors such as flexibility, solubility, stability, and pharmacokinetics, in addition to activity and selectivity. In approved drugs, majority of oxygen atoms are present within 4 Å from the COM of the molecule. This analysis offers valuable understanding of oxygen distribution, which could be used during the multiparameter optimization process, facilitating the transformation of a hit/lead compound into a potential drug candidate.

The chemical ordering and local atomic pressures in icosahedral Au N Al(N−42)Ni13 nanoalloys

Abstract The lowest energy chemical ordering configurations of 55 atom trimetallic AuNAl(N−42)Ni13 nanoalloys with Mackay icosahedron geometry were obtained through local relaxations performed using the Monte Carlo Basin-Hopping algorithm with the Gupta potential. This study explores how the chemical arrangement within an icosahedral structure influences the stability and mechanical properties of nanoalloys. In lowest energy structures, Ni atoms consistently occupy the icosahedral core, while Au and Al atoms stay on the surface, enhancing structural stability. Variations in mixing energy were evaluated to compare the relative stability of different compositions, with Au17Al25Ni13 identified as having the lowest mixing energy at the Gupta level. Furthermore, the icosahedral core, composed entirely of Ni atoms, experiences strong compressive stresses, while surface atoms are subjected to different pressures depending on the atom type and occupation site. Specifically, Au atoms located on the surface experience both tensile and compressive stresses, whereas Al atoms undergo lowest tensile stresses.

CO Adsorption on a Single‐Atom Catalyst Stably Embedded in Graphene

AbstractConfined single metal atoms in graphene‐based materials have proven to be excellent catalysts for several reactions and promising gas sensing systems. However, whether the chemical activity arises from the specific type of metal atom or is a direct consequence of the confinement itself remains unclear.

Stabilizing Single-Atom Catalysts on Metastable Phases of Transition Metal Dichalcogenides.

Single-atom catalysts have attracted a significant amount of attention due to their exceptional atomic utilization and high efficiency in a range of catalytic reactions. However, these systems often face thermodynamic instability, leading to agglomeration under the operational conditions. In this study, we investigate the interactions of 12 types of catalytic atoms (Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, Au, and Bi) on three crystalline phases (1T, 1T', and 2H) of six transition metal dichalcogenide layers (MoS2, MoSe2, MoTe2, WS2, WSe2, and WTe2) using first-principles calculations. We ultimately identify 82 stable single-atom systems that thermodynamically prevent the formation of metal clusters on these substrates. Notably, our findings reveal that the metastable 1T and 1T' phases significantly enhance the binding strength with single atoms and promote their thermodynamic stability. This research offers valuable insights into the design of stable single-atom systems and paves the way for the discovery of innovative catalysts.

Observation of Extraordinary Vibration Scatterings Induced by Strong Anharmonicity in Lead-Free Halide Double Perovskites.

Lead-free halide double perovskites provide a promising solution for the long-standing issues of lead-containing halide perovskites, i.e., the toxicity of Pb and the low stability under ambient conditions and high-intensity illumination. Their light-to-electricity or thermal-to-electricity conversion is strongly determined by the dynamics of the corresponding lattice vibrations. Here, the measurement of lattice dynamics is presented in a prototypical lead-free halide double perovskite(Cs2NaInCl6). The quantitative measurements and first-principles calculations show that the scatterings among lattice vibrations at room temperature are at the timescale of ≈1ps, which stems from the extraordinarily strong anharmonicity in Cs2NaInCl6. Further the degree of anharmonicity of each type of atom is quantitatively characterized in the Cs2NaInCl6 single crystal, which stems from the interatomic forces, and demonstrate that this strong anharmonicity is synergistically contributed by the bond hierarchy, the tilting of the NaCl6 and InCl6 octahedral units, and the rattling of Cs+ ions. Consequently, the crystalline Cs2NaInCl6 possesses an ultralow thermal conductivity of ≈0.43WmK-1 at room temperature, and a weak temperature dependence of T -0.41. These findings uncovered the underlying mechanisms behind the dynamics of lattice vibrations in double perovskites, which can largely benefit the design of optoelectronics and thermoelectrics based on halide double perovskites.

Soft nanoforest of metal single atoms for free diffusion catalysis.

Metal single atoms are of increasing importance in catalytic reactions. However, the mass diffusion is yet substantially limited by the confined surface of the support in comparison to homogeneous catalysis. Here, we demonstrate that cylindrical micellar brushes with highly solvated poly(2-vinylpyridine) coronas can immobilize 33 types of metal single atoms with 8.3 to 40.9 weight % contents on conventional electrodes under ambient conditions. This is favored by the forest-like hierarchically open soft structure of the micellar brushes and the dynamic coordination between the metals and the pyridine groups. It was found that the nanoforests of individual noble metal single atoms can be well solvated in an aqueous electrolyte to comprehensively expose the atomic active sites and the nanoforest of Pt single atoms on nickel foam reveals high electrochemical performance for hydrogen evolution. The micellar brush support also enables the simultaneous anchoring of multiple single atoms on the cathode of an anion-exchange membrane electrolyzer for long-term stable water electrolysis.

CO Adsorption on a Single-Atom Catalyst Stably Embedded in Graphene.

Confined single metal atoms in graphene-based materials have proven to be excellent catalysts for several reactions and promising gas sensing systems. However, whether the chemical activity arises from the specific type of metal atom or is a direct consequence of the confinement itself remains unclear. In this work, through a combined density functional theoryand experimental surface science study, we address this question by investigating Co and Ni single atoms embedded in graphene (Gr) on a Ni(111) support. These two single atom catalysts (SACs) exhibit opposite behavior toward carbon monoxide (CO) gas molecules: at RT, CO binds stably to Co, whereas it does not to Ni. We rationalize this difference by the energy position oftrapped metal dxz and dyz states involved in π backdonation to CO: while for Co, these states lie at the Fermi level, for Ni are located deep below it. This conclusion is corroborated by a proof-of-concept experiment, where a Gr/Ni(111) sample containing both stable Ni and Co single atoms was exposed to a CO partial pressure of 5 ‧ 10-7 mbar. Scanning tunnelling microscopy (STM), X-ray photoelectron spectroscopy (XPS), and temperature programmed desorption (TPD) measurements confirm the selective adsorption of CO on Co at RT.

Integrated analysis of cellulose structure and properties using solid-state low-field H-NMR and photoacoustic spectroscopy

In this study, we explore the structural intricacies of cellulose, a polymer composed of glucose monomers arranged in a linear chain, primarily investigated through solid-state NMR techniques. Specifically, we employ low-field proton nuclear magnetic resonance (H-NMR) to delve into the diverse hydrogen atom types within the cellulose molecule. The low-field H-NMR technique allows us to discern these hydrogen atoms based on their distinct chemical shifts, providing valuable insights into the various functional groups present in cellulose. Our focus extends to the examination of anomeric protons of glucose units and protons linked to carbon atoms engaged in glycosidic linkages within cellulose chains, which exist in diverse crystalline and amorphous forms. Solid-state low-field H-NMR spectroscopy aids in characterizing the crystallinity degrees and amorphous regions within cellulose, revealing time-dependent changes in free induction decay (FID) signals. Complementing this, we investigate the photo-absorption properties of cellulose fibers under both continuous and modulated irradiation using reversed double-beam photoacoustic spectroscopy (RDB-PAS). This photoacoustic approach allows us to observe ultraviolet- and visible light-induced processes, including electron trap filling and reductive changes on the fiber surface. Our findings suggest that RDB-PAS is a feasible method for estimating the electron trap distribution, serving as a potential measure of the density of crystalline cellulose defects. This integrated approach of combining solid-state low-field H-NMR and RDB-PAS techniques offers a comprehensive understanding of cellulose structure and properties, enhancing our ability to characterize its diverse features.

Extending the MST Model to Large Biomolecular Systems: Parametrization of the ddCOSMO-MST Continuum Solvation Model.

Continuum solvation models such as the polarizable continuum model and the conductor-like screening model are widely used in quantum chemistry, but their application to large biosystems is hampered by their computational cost. Here, we report the parametrization of the Miertus-Scrocco-Tomasi (MST) model for the prediction of hydration free energies of neutral and ionic molecules based on the domain decomposition formulation of COSMO (ddCOSMO), which allows a drastic reduction of the computational cost by several orders of magnitude. We also introduce several novelties in MST, like a new definition of atom types based on hybridization and an automatic setup of the cavity for charged regions. The model is parametrized at the B3LYP/6-31+G(d) and PM6 levels of theory and compared to the performance of IEFPCM/MST. Then, we demonstrate the robustness of the parametrization on the SAMPL2, SAMPL4, and C10 datasets. The ddCOSMO/MST models provide errors of ~0.8 and ~3.2 kcal/mol for neutrals and ions, respectively, showing a remarkable balanced and accurate description of cations and anions.

Fine-tuning molecular mechanics force fields to experimental free energy measurements.

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data. In this study, we treat a pretrained GNN force field-here, espaloma-0.3.2-as a foundation simulation model and fine-tune its charge model using limited quantities of experimental hydration free energy data, with the goal of assessing the degree to which this can systematically improve the prediction of other related free energies. We demonstrate that a highly efficient "one-shot fine-tuning" method using an exponential (Zwanzig) reweighting free energy estimator can improve prediction accuracy without the need to resimulate molecular configurations. To achieve this "one-shot" improvement, we demonstrate the importance of using effective sample size (ESS) regularization strategies to retain good overlap between initial and fine-tuned force fields. Moreover, we show that leveraging low-rank projections of embedding vectors can achieve comparable accuracy improvements as higher-dimensional approaches in a variety of data-size regimes. Our results demonstrate that linearly-perturbative fine-tuning of foundation model electrostatic parameters to limited experimental data offers a cost-effective strategy that achieves state-of-the-art performance in predicting hydration free energies on the FreeSolv dataset.

Evaluating long-range orientational ordering of water around proteins: signature of a tug-of-war scenario.

Long-range perturbations of water structure and dynamics by biomolecules are of great interest owing to their potential role in biomolecular recognition. In this article, we examined the local and long-range orientational structure of water molecules surrounding proteins with different total charges (+8, 0 and -8), both with and without the presence of a physiological salt environment. A prominent population of in-oriented water molecules was observed in the first hydration shell of the proteins, irrespective of their total charges. Starting from the third hydration layer, water molecules primarily reflected the total charge of the respective protein. This long-range ordering persisted up to the ninth hydration layer without a physiological salt environment and vanished beyond the fifth hydration shell in the presence of a physiological salt environment. Long-range orientational ordering around different types of surface atoms of a protein showed a particularly rich and heterogeneous behaviour. When the surface atom's charge and the protein's total charge were opposite, a clear signature of a tug-of-war was demonstrated in the long-range orientational ordering of water molecules. While water molecules reported the surface atom's charge at shorter distances, at longer distances, water molecules reported the total charge of the protein, with a crossover occurring around 10 Å. This phenomenon persisted even in the presence of a physiological salt environment. Evidence of destructive/constructive superposition of water-mediated orientation waves originating from two individual proteins with similar/opposite total charges was also demonstrated. These results are important for understanding long-range water-mediated recognition phenomena among biomolecules (e.g., protein-protein, protein-ligand, and protein-DNA interactions).

TopMT-GAN: a 3D topology-driven generative model for efficient and diverse structure-based ligand design.

Recent advancements in 3D structure-based molecular generative models have shown promise in expediting the hit discovery process in drug design. Despite their potential, efficiently generating a focused library of candidate molecules that exhibit both effective interactions and structural diversity at a large scale remains a significant challenge. Moreover, current studies often lack comprehensive comparisons to high-throughput virtual screening methods, resulting in insufficient evaluation of their effectiveness. In this study, we introduce Topology Molecular Type assignment (TopMT-GAN), a novel approach using Generative Adversarial Networks (GANs) for direct structure-based design. TopMT-GAN employs a two-step strategy: constructing 3D molecular topologies within a protein pocket with one GAN, followed by atom and bond type assignment with a second GAN. This integrated approach enables TopMT-GAN to efficiently generate diverse and potent ligands with precise 3D poses for specific protein pockets. When tested on five diverse protein pockets, TopMT-GAN exhibits promising and robust performance, demonstrating a potential enrichment of up to 46 000 fold compared to traditional high-throughput virtual screening methods. This highlights its potential as a powerful tool in early-stage drug discovery, such as hit and lead generation.

In Ukrainian

The molecular models of tin dioxide nanoparticles containing 1 – 10 metal atoms and can include coordinated or constitutive water were constructed. Their equilibrium spatial structure and electronic structure are calculated using the second-order Möller – Plesset perturbation theory with the SBKJC valence basis set. It is shown that the length of the Sn–O bond in nanoclusters does not depend on their size and the coordination number of Sn atoms, but is determined by the coordination type of neighboring oxygen atoms. Namely, the Sn–O(3) bond length (~ 2.10 Å) > the Sn–O(2) bond length (~ 1.98 Å). The obtained Sn–O(3) bond lengths are in good agreement with the experimental values for crystalline SnO2 samples (2.05 Å). The calculated atomization energy for SnO2 is 1661 kJ/mol and satisfactorily corresponds to the experimentally measured specific atomization energy of crystalline SnO2 (1381 kJ/mol). It was found that a satisfactory reproduction of the experimental characteristics of crystalline tin dioxide is possible when using clusters containing at least 10 tin atoms, for example, (SnO2)10×14H2O. Based on the analysis of the energy effects of coordination of water molecules and hydroxide ion, proton removal and proton transfer on the hydrated surface of tin dioxide, quantitative estimates of the acid-base characteristics of the active centers of the SnO2 surface were made. The dependence of the acidity of hydroxyl groups and coordinated water molecules on the coordination number of the oxygen atom and the neighboring tin atom, as well as on the size of the cluster model, was revealed. It has been shown that the acidity of proton and aproton centers naturally decreases with increasing coordination number of the tin atom. The methodology used in this work to calculate the рКа value of the smallest model of the SnO2×H2O composition allows us to reproduce the experimental data for stannous acids. The mechanisms of formation of the simplest nanostructures from the initial forms of stannous hydroxide Sn(OH)4 are proposed. It has been shown that the formation of a dimer (SnO2)2×4H2O by the association of two Sn(OH)4 molecules is energetically most advantageous. Further transformations of nanoparticles lead to an increase in their size, dehydration, and the formation of denser structures that have crystallinity features inherent in solid-phase SnO2.

Determining the optimal oxidation temperature of non-isothermal liquid fuels injections using modeling based on statistical droplet distribution

Single-hole injections of liquid hydrocarbon fuels (isooctane and dodecane) under high turbulence have been investigated using direct numerical simulation based on the statistical model considering the droplets’ atomization, distribution, and combustion. The study objects are the heat and mass transfer processes during atomization and combustion of liquid fuels injections within the combustion chambers of thermal engines. The temperature and carbon dioxide concentration distributions of the fuel-air mixture, the distributions of the droplets, their velocities, and the Sauter mean radius within the isooctane and dodecane oxidation in the engine’s combustion space were obtained. An investigation of the oxidizer’s initial temperature influence on the droplets’ atomization and combustion processes showed that the optimal temperature for both fuels is 900 K. The obtained modeling results were confirmed in good agreement with theoretical and experimental data. Thanks to the integrated use of approaches from statistical theory, numerical algorithms and 3D computer modeling techniques, the results obtained are distinguished by high accuracy, efficiency in reducing computational resources, scientific novelty in the type of droplet atomization and suitability for practical application for technological solutions not only for single-hole, but also for multi-hole injections of liquid fuels and studying the jet-to-jet interaction phenomena. The obtained research results can be applied in miscellaneous internal combustion engines development with different atomization types, which will allow us to contemporaneously settle the concerns of streamlining the combustion process, improving the completeness of fuel combustion and reducing emissions of harmful substances

Ternary molybdate Rb5(Ag1/3Hf5/3)(MoO4)6: synthesis, structure, thermal expansion and ionic conductivity

This study aims to characterize the properties of the ternary molybdate Rb5(Ag1/3Hf5/3)(MoO4)6, which was previously identified during phase equilibrium investigations in the Ag2MoO4–Rb2MoO4–Hf(MoO4)2 system. A ternary molybdate Rb5(Ag1/3Hf5/3)(MoO4)6 was obtained through a solid-state reaction. It was found that the compound crystallizes in the trigonal space group R c and melts at 596 °C with decomposition. Its structure was refined using the Rietveld method. The crystal structure consists of a mixed framework composed of isolated (Ag/Hf)O6 octahedra and MoO4 tetrahedra, interconnected through shared oxygen vertices. Large voids within the framework accommodate two types of rubidium atoms. Using the powder XRD data recorded over 30–400 °C, the principal components of the thermal expansion tensor were determined. Rb5(Ag1/3Hf5/3)(MoO4)6 can be classified as a material with high thermal expansion coefficient (αV = 36.7∙10–6 °С–1 at 400 °C). At elevated temperatures, the compound exhibited significant ionic conductivity, reaching 1.7·10−3 S/cm at 480 °C with an activation energy Еа = 0.8 eV with oxygen ions as the probable charge carriers. Energy barriers for one-, two-, and three-dimensional transport in the compound were theoretically evaluated using the softBV program and bond valence sum maps (BVS).

Complex Structure, Chemical Bonding, and Electrical Transport Properties of a La-Doped Zintl Phase

The La-doped ternary Zintl phase Ca10.43(3)La0.57Sb9.69(1) was successfully synthesized by arc melting, and the title compound adopted the Ho11Ge10-type structure with a tetragonal I4/mmm space group (Z = 4, Pearson code tI84). The complex crystal structure is composed of (1) the four different kinds of cationic Ca or Ca/La mixed sites surrounded by seven or nine Sb atoms and (2) the 3-dimensional cage-shaped anionic frameworks built by the other two types of Sb atoms. In particular, the La dopants preferred to occupy the Ca4 and Ca1 sites, and this specific cationic-site preference can be rationalized by both electronic and size-factor criteria. Moreover, the ca. 16% occupational deficiency observed at the Sb3 site was attributed to the energetically unfavorable antibonding character of the Sb3–Sb3 bond in the [Sb3]4 tetramers, according to a series of DFT calculations. A crystal Hamilton overlap population curve analysis also proved that the title compound Ca10.43(3)La0.57Sb9.69(1) tried to keep the valence electron count below 71.02 to remain energetically stable in the Ho11Ge10-type phase. Measurements of temperature-dependent electrical transport properties revealed that the La doping indeed enhanced the electrical conductivity of Ca10.43(3)La0.57Sb9.69(1) compared to the un-doped Ca11Sb10. However, unlike other rare earth metal (RE)-doped compounds in the Ca11−xRExSb10 (RE = Nd and Sm) system that display semiconducting behavior, the La-doped title compound showed poor metallic electrical properties. The positive values of Seebeck coefficients indicated the p-type character of the title compound despite the successful n-type La doping, and this should be attributed to Sb deficiency.

Effects of Ti and Sn Substitutions on Magnetic and Transport Properties of the TiFe2Sn Full Heusler Compound

The synthesis of polycrystalline TiFe2Sn samples by a route including arc melting and spark plasma sintering with Hf, Y, and In substitutions at the Ti and Sn sites is investigated. For a reduced amount of substitution, around 2 at%, the samples are single phase, while for increased amounts, secondary phases segregate. As is characteristic of these compounds, the Fe-Ti atomic disorder generates a weak ferromagnetic ordering, which is also influenced by the type of substitutional atoms and the secondary phases in the samples with a higher Hf content. The Seebeck coefficient values show an increase for Ti0.98Hf0.02Fe2Sn and for samples with an adjusted Sn content, resulting in slightly increased power factor values. These values reach a maximum for Ti0.98Hf0.02Fe2Sn at approximately 300 K and for TiFe2Sn1.05 at approximately 325 K, namely, 2.69 × 10⁻4 Wm−1K−2 and 2.52 × 10⁻4 Wm−1K−2, respectively. The thermal conductivity of all the samples with substitutions increases with respect to the pristine sample. The highest figure of merit value of 0.016 is also obtained for Ti0.98Hf0.02Fe2Sn at 325 K.

Predicting the diffusion coefficients of rejuvenators into bitumens using molecular dynamics, machine learning, and force field atom types

This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. The simulations cover three bitumen types (NO, TO, FO), five aging degrees, and four temperatures (60 °C, 120 °C, 160 °C, 200 °C), capturing diffusion coefficients ranging from 0.0068e-10 m2/s in highly aged bitumens at 60 °C to 4.35e-10 m2/s in fresher samples at 200 °C. The MLM, built with 18 chemical descriptors for bitumen and rejuvenator sides, achieves an R2 of 0.97, accurately predicting diffusion across varied conditions. This approach abstracts away from the need for repeated MD simulations, enabling diffusion predictions even for systems outside the original dataset. The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. It also demonstrates how the model offers a practical framework for understanding the diffusion and performance of rejuvenators by linking time-dependent factors—such as concentration, depth, and rejuvenation time—with the bulk properties of bitumen-rejuvenator systems, facilitating industrial applications

First-principles study on TiC/TiN heterogeneous nucleation interface in high-titanium steel

A systematic study using the first-principles method was conducted to investigate the interface relationship between TiC and TiN in high-titanium steel, where TiC nucleates and grows with TiN as the core. The results showed that the energy of the (111) surface depends on the type of terminal atoms, with the Ti-terminated surface exhibiting lower surface energy than the C(N)-terminated surface. In contrast, the energy of (100) and (110) surfaces was independent of the terminal atom type. The order of surface energy determined to be (111)-Ti<(100)<(110)<(111)-C(N), the work function exhibits the same trend, while the stability is exactly the opposite. Among all interface structures, the interface structure with Ti-C terminal atomic combination was identified as the most stable, which can be attributed to the formation of covalent, ionic, and metallic bonds at the interface. The bonding strength primarily arises from the hybridization between Ti-d orbitals and C(N)-p orbitals. Within the (111) interface structures, metallic bonds were formed in the interface structure with the Ti-Ti terminal atomic combination, leading to the lowest interface energy and making it thermodynamically the most stable. Consequently, TiC is capable of heterogeneously nucleating on TiN, with the preferred nucleation surface of TiN following the order of (111)>(100)>(110).