Journal of Molecular Modeling最新文献

{"title":"Ultrafast terahertz-induced torque disruption of fentanyl's μ-opioid receptor binding for precision overdose reversal.","authors":"Moses G Udoisoh, Olusola Olaitan Adegoke, Amy Lebua James","doi":"10.1007/s00894-025-06447-z","DOIUrl":"10.1007/s00894-025-06447-z","url":null,"abstract":"<p><strong>Context: </strong>This study establishes a quantum-biophysical framework for non-invasive opioid overdose reversal by demonstrating ultrafast terahertz (THz) torque-mediated disruption of fentanyl-μ-opioid receptor (μOR) binding. By targeting the vibrational modes of the fentanyl-μOR complex with resonant THz pulses (1-1.5 THz, ≥ 100 kV/cm), the study examines two key binding configurations: the Asp147 salt bridge (D147) and His297 hydrogen bond (H297). The model reveals that THz-induced torque reduces the dissociation barrier by 3.2-3.8 kcal/mol through mechanical disruption of the N-H⁺···O⁻ interaction, achieving 50% unbinding within 1.2 ps at optimal frequencies. The H297 configuration dissociates 40% faster than D147, indicating a pharmacologically preferable site for intervention. A sigmoidal dose-response is observed in the 100-150 kV/cm range, enabling > 90% dissociation efficacy under non-thermal conditions. These findings offer a novel electromagnetic approach for modulating opioid pharmacodynamics and inform the development of receptor-targeted antidotes via precision bioelectromagnetic strategies. While this study demonstrates the theoretical feasibility of THz-induced dissociation, future experimental work is needed to address translational challenges such as tissue penetration and biological specificity.</p><p><strong>Methods: </strong>The study employs a quantum-classical hybrid framework combining time-dependent Schrödinger equation simulations with classical electrodynamics. Fentanyl is modeled as a confined asymmetric rotor interacting with a µOR-like potential landscape under circularly polarized THz radiation. Quantum torque is derived from angular momentum operators coupled to the electric field vector. Site-specific binding configurations (D147 and H297) are simulated with field-driven vibrational excitation and potential energy surface deformation. Dissociation dynamics and barrier modulation are quantified using Fermi's Golden Rule and time-evolved wavepacket propagation. Numerical computations were performed in Wolfram Mathematica 13.1, with molecular input parameters validated against DFT-based dipole moments, mass tensors, and force-field data extracted from experimental literature.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"221"},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting SARS-CoV-2 main protease: a pharmacophore and molecular modeling approach.","authors":"Nitchakan Darai, Piyatida Pojtanadithee, Kamonpan Sanachai, Thierry Langer, Peter Wolschann, Thanyada Rungrotmongkol","doi":"10.1007/s00894-025-06441-5","DOIUrl":"10.1007/s00894-025-06441-5","url":null,"abstract":"<p><strong>Context: </strong>The COVID-19 pandemic, driven by SARS-CoV-2, has had a profound impact on global health, with severe respiratory complications being a primary concern. The main protease (Mpro) of SARS-CoV-2 plays a critical role in viral replication, making it an attractive target for therapeutic intervention. This study aimed to identify potential Mpro inhibitors using an integrated computational approach. From an initial pool of 89,200 compounds in the ChemDiv database, a systematic screening process reduced the candidates to 735 through drug-like property predictions and pharmacophore-based virtual screening. Molecular docking against four co-crystal structures of the inhibitor/Mpro complex, followed by molecular dynamics (MD) simulations and binding free energy calculations, identified E912-0363 and G740-1003 as promising candidates with binding affinities comparable to nirmatrelvir. Extended 500-ns MD simulations further established E912-0363 as a highly promising Mpro inhibitor, supporting its potential for therapeutic development as a complementary or alternative treatment to nirmatrelvir.</p><p><strong>Methods: </strong>Pharmacophore modeling and virtual screening were conducted using the ChemDiv database, reducing 89,200 compounds to 735 candidates based on drug-like property predictions. Molecular docking was performed against four SARS-CoV-2 Mpro co-crystal structures using AutoDock VinaXB and GOLD docking programs. The top five candidates (E912-0363, P635-0261, G740-1003, G069-0804, and 8602-0428) were subjected to 100-ns molecular dynamics (MD) simulations using the AMBER force field. Binding free energy calculations were performed using the MM/GBSA method. Extended 500-ns MD simulations were carried out for the most promising candidate, E912-0363, to evaluate its long-term stability and interaction with the Mpro binding site.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"222"},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protein chains in tight-binding framework.","authors":"Hamze Mousavi, Ronak Emami","doi":"10.1007/s00894-025-06450-4","DOIUrl":"10.1007/s00894-025-06450-4","url":null,"abstract":"<p><strong>Context: </strong>This research assesses the band structure and density of states for three unique conformations of protein chains, examined in both finite and infinite configurations. Under a constant temperature, the band structure and density of states reveal flat energy dispersion curves and discrete energy levels for the finite protein conformations, which are ascribed to the presence of localized states within these structures. Conversely, the infinite protein chain demonstrates a continuous band structure due to its periodic nature, resulting in narrow-gap semiconducting behavior across all conformations. The influence of temperature on the energy spectra of the systems, regardless of the configuration type, leads to alterations in both the peak heights and positions for all three protein conformations.</p><p><strong>Methods: </strong>The exploration of the electronic properties of protein chains is performed using the tight-binding Hamiltonian method in conjunction with Green's function formalism. The primary emphasis is placed on protein chains consisting of thirty-six amino acids, characterized by a straightforward structural arrangement, where amino acids are interconnected through covalent bonds, while the other two conformations exhibit a more complex structural configuration, with amino acids linked by both peptide bonds and non-covalent interactions.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"223"},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical investigation of solvent-polarity-dependent excited-state intramolecular proton transfer behavior for incorporated bulky -CF₃ side semi-aliphatic polyimide.","authors":"Xiaoyu Zhang, Qingfang Zhang, Yujian Zhang, Qiuhe Ren, Hengyi Yuan","doi":"10.1007/s00894-025-06442-4","DOIUrl":"10.1007/s00894-025-06442-4","url":null,"abstract":"<p><strong>Context: </strong>In this work, the molecular properties of the novel semi-aliphatic polyimide derivative with incorporating bulky -CF₃ side groups (3H-6F) in gas, chloroform and acetonitrile have been studied by DFT and TDDFT methods. The optimal reaction path could be found to regulate the occurrence of excited-state intramolecular proton transfer (ESIPT) reaction. In S₁ state, the strength of O1-H2···O3 hydrogen bond increases significantly and contributes to the ESIPT reaction providing the driving force. We calculated the infrared (IR) vibrational spectrum to analyze the movement of O1-H2 bond expansion vibration and then studied the variations of hydrogen bonding strength. In addition, from the rearrangement of frontier molecular orbital (MOs), the electron density distribution should be also an extremely important positive factor in ESIPT process. According to potential energy curves (PECs), ESIPT reaction occurs after the molecule absorbs the photon to reach the first excited state, and the hydrogen atom of the O1-H2 bond combines with the adjacent O3 atom to form an isomer. After the completion of the ESIPT reaction process, the S₁ state returns to S₀ state with recovering the original structure. The barrier size and photoexcitation characteristics in different surroundings are also compared, based on which we present that the increase of solvent polarity promotes occurrence of ESIPT reaction process for 3H-6F fluorophore.</p><p><strong>Methods: </strong>All molecular structures have been optimized using DFT and TDDFT method with B3LYP/6-311 +  + G(d,p) level by Gaussian 16 software. Vertical excitation simulations were based on TDDFT method with analyzing charge redistributions. Using Multiwfn 3.8 software, the core-valence bifurcation indexes were performed. Further, potential energy surfaces have been constructed, based on which the transition state configurations were found at the same level.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"219"},"PeriodicalIF":2.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A classification method for fluorescence emission spectra of anionic surfactants with few-shot learning.","authors":"Hanyang Ning, Miao Ma, Zhiwei Shi, Liping Ding","doi":"10.1007/s00894-025-06440-6","DOIUrl":"10.1007/s00894-025-06440-6","url":null,"abstract":"<p><strong>Context: </strong>The unregulated use of anionic surfactants poses significant environmental risks, necessitating methods for their rapid and accurate identification. While fluorescence spectroscopy is a powerful tool, its application faces a critical challenge: existing analytical strategies either rely on complex and costly sensor arrays to acquire rich data, or they apply traditional machine learning to simpler, single-spectrum data, which often requires pre-processing steps like PCA that risk information loss. Furthermore, standard deep learning approaches are often unsuitable due to the high cost and effort required to acquire the large datasets they need for training. To address this gap, we propose an end-to-end, few-shot learning method (CNN-PN) for the classification of anionic surfactant fluorescence emission spectra. Our approach leverages a one-dimensional convolutional neural network (1D-CNN) to automatically extract features from the full, raw spectrum, thus avoiding lossy pre-processing. It then employs a prototypical network to perform robust, similarity-based classification, a strategy highly effective for limited sample sizes. We validated our method on our FESS dataset (53 surfactant categories) and a public metal oxides dataset. In our experiments, the CNN-PN method consistently outperformed traditional techniques like LDA, SVM, and KNN. It achieved 76.36% accuracy when trained with only a single sample per class, 95.90% in a multi-sample scenario on our FESS dataset, and 84.86% on the public dataset. This work provides a powerful and data-efficient framework for spectral analysis, facilitating the development of more accessible and rapid fluorescence sensing technologies, particularly for applications where data collection is expensive or constrained.</p><p><strong>Methods: </strong>A few-shot learning classification method based on prototypical networks was employed. A one-dimensional convolutional neural network (1D-CNN) was utilized to extract spectral features from the full fluorescence emission spectra. Classification was then performed within the prototypical network framework using Euclidean distance as the similarity metric between features in the learned latent space. The Python programming language and the PyTorch library were used for all model implementations and data analysis.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"218"},"PeriodicalIF":2.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular dynamics simulations of thermal transport in metals using a two-temperature model.","authors":"B Baer, D G Walker","doi":"10.1007/s00894-025-06433-5","DOIUrl":"10.1007/s00894-025-06433-5","url":null,"abstract":"<p><strong>Context: </strong>In classical molecular dynamics, thermal transport via electrons is typically non-existent. Therefore, thermal property determination in metals or material systems that include metals is inaccessible. We have developed a two-temperature model for use with non-equilibrium molecular dynamics to predict thermal interface resistance across metal-metal and metal-insulator interfaces. Using LAMMPS and a modified module for the diffusion of thermal energy via electrons, we systematically examine the effects of including a second transport pathway through material systems. We found that inclusion of an electronic transport pathway reduces the phonon-only thermal conductivity because of electron-phonon scattering. Moreover, the presence of electrons eliminates temperature jumps at the boundary but still admits interface resistance, which is reduced in some cases by an order of magnitude.</p><p><strong>Method: </strong>We developed a module for LAMMPS that estimates thermal transport via the diffusion equation with a specified electron thermal conductivity. The electronic energy is transferred to/from the atomic system using velocity rescaling with appropriate momentum perturbation. The atomistic motion is governed by the NiU3-EAM potential.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"220"},"PeriodicalIF":2.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12296977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In depth first-principles investigation of phase stability, structural, vibrational, electronic, elastic, piezoelectric, and magnetic properties in vanadium-based janus dichalcogenide monolayer VBrSe.","authors":"Laichaoui Mahdi Mourad, Rami Mrad, Yuanping Chen, Shibing Chu","doi":"10.1007/s00894-025-06444-2","DOIUrl":"10.1007/s00894-025-06444-2","url":null,"abstract":"<p><strong>Context: </strong>This study presents a comprehensive first-principles investigation of the structural, electronic, vibrational, elastic, and piezoelectric properties of monolayer Janus VBrSe in both 2H and 1 T phases. The 1 T phase is found to be dynamically unstable, whereas the 2H-VBrSe phase is confirmed to be both energetically favorable and dynamically stable, indicating its feasibility for experimental synthesis. The 2H phase exhibits a direct band gap with pronounced strain sensitivity, significant out-of-plane piezoelectric response, and distinct Raman-active vibrational modes, facilitating phase identification. Micromagnetic simulations further reveal robust ferromagnetic ordering. These properties establish 2H-VBrSe as a multifunctional material suitable for next-generation applications in sensors, optoelectronics, flexible devices, and spintronic systems.</p><p><strong>Methods: </strong>Density functional theory (DFT) calculations were performed using the VASP package, incorporating spin-orbit coupling and van der Waals corrections to accurately capture the behavior of layered systems. Electronic structure and geometry were optimized using advanced exchange-correlation functionals to improve band gap accuracy. Phonon dispersion analyses confirmed dynamic stability, while elastic constants and piezoelectric coefficients were computed to assess mechanical and electromechanical performance. Ferromagnetic behavior was evaluated via micromagnetic simulations using MuMax3. The theoretical framework enables further exploration of temperature-dependent phenomena, such as thermal stability and dynamical response, through ab initio molecular dynamics.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"217"},"PeriodicalIF":2.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noncovalent dyads of lanthanide nitride cluster fullerenes Ln₃N@C₈₀ and bisphthalocyanines LnPc₂: Insights from DFT calculations.","authors":"Lina M Bolivar-Pineda, Elena V Basiuk, Vladimir A Basiuk","doi":"10.1007/s00894-025-06415-7","DOIUrl":"10.1007/s00894-025-06415-7","url":null,"abstract":"<p><strong>Context: </strong>Lanthanide-based systems, such as nitride cluster fullerenes Ln₃N@C₈₀ and bipthalocyanines LnPc₂ (Pc = phthalocyanine ligand), are of interest for their magnetic, fluorescent and electronic properties. In this regard, we performed DFT characterization to investigate the changes in structure and electronic properties for noncovalently interacting lanthanide (Ln; where Ln = La, Ce, Gd and Lu) nitride cluster fullerenes and bisphthalocyanines to form Ln₃N@C₈₀ + LnPc₂ dyads. The optimized geometries, formation and frontier orbital energies, HOMO-LUMO plots, charge and spin of Ln and N(NCF) atoms, as well as spin density plots of the dyads were analyzed in comparison with those of isolated Ln₃N@C₈₀ and LnPc₂ components. In addition to LnPc₂ bending distortion, the noncovalent dyad formation alters the geometry of the encapsulated Ln₃N cluster, favoring more planar or pyramidal geometries, depending on the case. The HOMO and LUMO orbitals are found on bisphthalocyanines, being localized on the isoindole units, except for Ce₃N@C₈₀ + CePc₂ dyad, where the LUMO was found on the central metal of CePc₂. The HOMO-LUMO gap energy is lower for the dyads compared to isolated NCFs, being rather close to the gap energy of bisphthalocyanines. The changes in spin density distribution are evident in the dyads containing Ce and Gd atoms, contrary to their La and Lu-derived counterparts. The interaction of Ce₃N@C₈₀ and Gd₃N@C₈₀ with CePc₂ and GdPc₂, respectively, causes redistribution of the spin density, with changes in the orientation of spin-up and spin-down electrons in the encapsulated Ce₃N and Gd₃N clusters.</p><p><strong>Methods: </strong>The geometry optimization and electronic properties calculations based on density functional theory were performed using the DMol³ module of Material Studio 8.0 software package from Accelrys Inc. The computational parameters selected included the general gradient approximation functional PBE, combined with a long-range dispersion correction developed by Grimme (PBE-D2), the double numerical basis set (DN), equivalent to the 6-31G Pople-type basis set along with the DFT semiconductor pseudopotentials. To mitigate the self-consistent field convergence problems, the thermal smearing technique was applied, with a final very small value of 0.0001 Ha (equivalent to 31.6 K temperature), or Fermi orbital occupancy in some cases.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"216"},"PeriodicalIF":2.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144697338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-fidelity neural network-based prediction of tensile strength of high-entropy alloy (FeNiCoCrCu) using molecular dynamics data.","authors":"Nazib E Elahi Khan Chowdhury, Alif Jawad, Anfalur Rahman, Mohammad Jane Alam Khan","doi":"10.1007/s00894-025-06439-z","DOIUrl":"https://doi.org/10.1007/s00894-025-06439-z","url":null,"abstract":"<p><strong>Context: </strong>High-entropy alloys (HEAs) represent a class of advanced materials with superior mechanical, thermal, and chemical properties. FeNiCoCrCu HEA has been of particular interest due to its excellent tensile strength, corrosion resistance, and thermal stability. However, it is a significant challenge to understand and optimize the mechanical properties of such alloys due to the complex structure. Molecular dynamics (MD) is a popular choice in investigating atomic-scale characteristics but is computationally costly for large polycrystal systems. Machine learning approaches have seen growing interest as surrogate models that can produce accurate predictions and lower computational costs. This study demonstrates the first application of Multi-fidelity Physics Informed Neural Network (MPINN) model for predicting the tensile strength of FeNiCoCrCu. This study generates a large dataset of tensile strength for different compositions of FeNiCoCrCu HEA and uses it to train a MPINN model. The MPINN model successfully predicts the tensile strength of FeNiCoCrCu for different compositions and validates the effectiveness of the MD data-enabled MPINN model in making accurate predictions of material properties.</p><p><strong>Methods: </strong>This study uses LAMMPS for the molecular dynamics simulations and TensorFlow for building and running the machine learning models. The low-fidelity (LF) and high-fidelity (HF) data for the machine learning model are obtained from MD simulations of small single crystals and large polycrystals, respectively. MD simulation systems are created using Atomsk, and EAM potential is used for the forcefield. The simulations are visualized using OVITO. The MPINN model utilizes both linear and non-linear relations between LF and HF data. In TensorFlow, the machine learning model is optimized using the Adam optimizer, and L2 regularization is used to prevent overfitting.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"214"},"PeriodicalIF":2.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New determination of the thermophysical properties of argon gas considering nuclear spin and symmetry effects.","authors":"F Bouchelaghem, H Boutarfa, M Chicouche, S Lias","doi":"10.1007/s00894-025-06435-3","DOIUrl":"https://doi.org/10.1007/s00894-025-06435-3","url":null,"abstract":"<p><strong>Context: </strong>Since statistical physics and quantum mechanics were first successfully combined thanks in part to the work of Chapman and Cowling and Hirschfelder. Extensive theoretical and experimental research has been dedicated to understanding the kinetics of gases and gas mixtures. This integration has, among other achievements, theoretically established a direct link between the macroscopic properties of gases whether measured or calculated and the quantum characteristics of their constituent particles. This model successfully established straightforward mathematical relationships linking the microscopic interactions between the atomic and/or molecular components of a gas to measurable transport properties, such as diffusion and viscosity coefficients. It also provided explanations for how these properties vary and how they are influenced by thermodynamic parameters like pressure, density, and temperature.</p><p><strong>Methods: </strong>The potential data available to us are either obtained from ab initio calculations or experimental measurements. The ab initio values of the potential V(R) are derived from a quantum-theoretical approach to the molecular problem. Typically, these methods provide the potential energy at discrete values of the internuclear distance R within a specified range. To build the potential energy curve corresponding to the fundamental interactions, we will rely on ab initio data. Knowing this potential allows for the numerical solution of the radial wave equation using Numerov's method, ultimately enabling the calculation of the phase shifts <math><mrow><mi>η</mi> <mfenced><mi>E</mi></mfenced> </mrow> </math> . From the elastic collision phase shifts, we derive the self-diffusion coefficient D, viscosity <math><mi>η</mi></math> , and thermal conductivity <math><mi>λ</mi></math> using the Chapman-Enskog model. For diffusion and viscosity, we perform calculations both with accounting for the symmetry and spin effects associated with the identical nature of the colliding particles. We then examine how these transport coefficients vary with temperature and propose a straightforward computational approach to obtain analytical expressions for D(T), <math><mrow><mi>η</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo></mrow> </math> , and <math><mrow><mi>λ</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo> <mo>.</mo></mrow></math></p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"215"},"PeriodicalIF":2.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}