Journal of Computational Chemistry最新文献

{"title":"The Paramagnetic Properties of Breathing Metal-Organic Frameworks: Theoretical Models for Paddle-Wheel Secondary Building Units of Cobalt, Nickel, and Copper.","authors":"Maxim R Ryzhikov,Svetlana G Kozlova","doi":"10.1002/jcc.70208","DOIUrl":"https://doi.org/10.1002/jcc.70208","url":null,"abstract":"The metal-organic frameworks (MOFs) that contain paramagnetic metal cations at their secondary building units (SBUs) have potential applications in magnetic materials for single molecule magnets, molecular spintronics, and quantum computing. The quantum chemical investigations were carried out on the systems M2(O2CH)4 and M2(O2CH)4(C6H12N2)2, which model paddle-wheel SBUs embedded in breathing paramagnetic MOFs, specifically M2(bdc)2(dabco) (dabco = C6H12N2, bdc2- = C8H4O4 2-, M2+ = Co2+, Ni2+, Cu2+). An analysis of the electronic states and interatomic interactions for these models was performed and compared with experimental data on heat capacity and magnetic susceptibility of M2(bdc)2(dabco). It is shown that the antiparallel ordering (broken symmetry) of the spins on the metal atoms is the lowest spin state of the model systems. The coordination of the dabco molecules makes the high-spin state the second most stable spin state, with a relatively small energy separation from the broken symmetry solution.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"8 1","pages":"e70208"},"PeriodicalIF":3.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144796714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling Microscopic Mechanisms of Chemical Mechanical Polishing via Multi-Scale Theoretical Calculations.","authors":"Bin Hu,Xinhuan Niu,Jiakai Zhou,Changxin Dong,Chao He,Xinjie Li,Zheng Wu,Jiahui Li","doi":"10.1002/jcc.70213","DOIUrl":"https://doi.org/10.1002/jcc.70213","url":null,"abstract":"Chemical mechanical polishing (CMP) is a critical planarization technique that combines chemical reactions and mechanical grinding. However, analyzing its underlying mechanisms at the microscopic level, particularly on the wafer surface, remains a significant challenge. This review focuses on the theoretical study of the micro-mechanism of CMP, and systematically reviews the application of quantum chemistry based on density functional theory (DFT) and molecular dynamics (MD) based on Newtonian mechanics (classical MD/reaction MD/ab initio MD) in the prediction of reaction activity and the analysis of interface behavior. DFT calculation can efficiently locate active sites and reveal the nature of bonding; MD simulation has realized the leap from adsorption configuration to reaction path, but it faces challenges such as limited time scale and high computational cost. So, the development of accurate force field for CMP complex solution environment, the combination of DFT calculation and MD simulation, and the application of machine learning MD will become the breakthrough points for CMP theoretical calculation. The deep integration of theoretical calculation and experiment will subvert the traditional trial-and-error research and development mode of CMP, and realize the whole process digital chain of \"molecular design -performance prediction - defect detection\" in computer software, which is expected to accelerate the green revolution in precision manufacturing fields such as semiconductors.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"1 1","pages":"e70213"},"PeriodicalIF":3.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research Advance in the Development of Antimicrobial Peptides Using Deep Learning","authors":"Yuchen Hu,&nbsp;Junchao Zhou,&nbsp;Yuhang Gao,&nbsp;Ban Chen,&nbsp;Jiangtao Su,&nbsp;Hong Li,&nbsp;Wen Wu","doi":"10.1002/jcc.70203","DOIUrl":"10.1002/jcc.70203","url":null,"abstract":"<div>\u0000 \u0000 <p>As the global issue of antibiotic resistance becomes increasingly severe, antimicrobial peptides (AMPs), a class of short-chain peptides with broad-spectrum antibacterial activity, have garnered significant attention from the scientific community due to their unique antibacterial properties and potential clinical applications. However, traditional methods for discovering and designing AMPs often rely on repetitive laboratory trials and error corrections, which are not only costly but also inefficient. In contrast, the application of artificial intelligence (AI) technology, particularly deep learning algorithms, for screening and predicting AMPs has demonstrated substantial advantages. Deep learning models can automatically learn and extract key features of AMPs from large-scale datasets, enabling efficient prediction of potential AMP sequences. This approach not only significantly enhances the screening efficiency of AMPs but also reduces research and development costs, thereby opening new avenues for the study and application of AMPs. Therefore, this article provides an overview of the workflow and research progress in utilizing deep learning to predict AMP sequences. The limitations and challenges faced by this technology in the field of AMP prediction are also discussed in this review.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144825810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methyllithium “On-Water” Addition on Ketone: H-Bond Network and Mechanism","authors":"Samuel D. Mador,&nbsp;Anne MILET","doi":"10.1002/jcc.70200","DOIUrl":"10.1002/jcc.70200","url":null,"abstract":"<div>\u0000 \u0000 <p>Organolithium compounds play a pivotal role in organic synthesis, yet their high reactivity and extreme moisture sensitivity—due to the polar carbon-lithium bond—typically necessitate strictly anhydrous conditions. Intriguingly, recent studies have demonstrated that the addition of water can facilitate certain organolithium reactions, such as the efficient synthesis of 2,2-disubstituted tetrahydrofurans via the reaction of methyllithium (MeLi) with 4-chloro-1-phenylbutan-1-one under “on-water” conditions. Despite the success of such transformations, the underlying reaction mechanisms and the behavior of MeLi in aqueous environments remain poorly understood. In this work, we study the MeLi-mediated reaction in the presence of water using hybrid quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations, biased by well-tempered metadynamics within a water-ether droplet solvation model. Both dimeric and tetrameric MeLi clusters were examined. Our findings reveal that a hydrogen-bond network involving partially hydrolysed dimers (MeLi₂OH) and diffusing water molecules stabilizes the organolithium species within the ether phase, preventing complete hydrolysis and enabling product formation. Similarly, the partially hydrolysed tetramer cluster MeLi₄(OH)₃ was also found to support the progression of the addition reaction without full decomposition.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144825811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Q-DFTNet: A Chemistry-Informed Neural Network Framework for Predicting Molecular Dipole Moments via DFT-Driven QM9 Data","authors":"Dennis Delali Kwesi Wayo,&nbsp;Mohd Zulkifli Bin Mohamad Noor,&nbsp;Masoud Darvish Ganji,&nbsp;Camila Martins Saporetti,&nbsp;Leonardo Goliatt","doi":"10.1002/jcc.70206","DOIUrl":"https://doi.org/10.1002/jcc.70206","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;This study presents Q-DFTNet, a chemistry-informed neural network (ChINN) framework designed to benchmark graph neural networks (GNNs) for dipole moment prediction using the QM9 dataset. Seven GNN architectures, GCN, GIN, GraphConv, GATConv, GATNet, SAGEConv, and GIN+EdgeConv, were trained for 100 epochs and evaluated across performance and interpretability metrics. GraphConv achieved the lowest test MSE (0.7054), MAE (0.6196), and the highest &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; (0.6513) with only 16.5k trainable parameters, confirming its optimal accuracy-complexity trade-off. GIN+EdgeConv followed closely with MSE of 0.7386, MAE of 0.6332, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of 0.6349, leveraging edge-awareness for enhanced expressivity. In contrast, attention-based models like GATConv and GATNet underperformed, with test MSEs of 0.9667 and 1.0096, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; values of 0.5221 and 0.5009, despite their higher complexity (43.5k and 37.3k parameters). Latent space analysis via t-SNE, PCA, and UMAP showed superior cluster separability for GraphConv, GIN+EdgeConv, and GCN. Clustering metrics corroborated these observations: GraphConv achieved a Silhouette Score of 0.4665, a Davies–Bouldin Index of 0.7111, and a Calinski–Harabasz Score of 1278.40. Cluster-wise molecular dipole means for GIN+EdgeConv ranged from 2.6221 to 2.9606 Debye, reflecting high semantic coherence. Residual analysis and QQ plots confirmed that models with lower MSEs also had near-Gaussian error distributions, enhancing interpretability. Compared to benchmark models like PhysNet and DimeNet++, Q-DFTNet offers lower absolute accuracy but excels in modularity, interpretability, and computational efficiency. For a chemically grounded baseline for deploying GNNs in quantum chemistry and materials discovery pipelines, Q-DFTNet is ","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vibronic Coupling in Formamide Radical Cation: A Full Dimensional Quantum Mechanical Study","authors":"Yarram Ajay Kumar,&nbsp;Mamilwar Rani,&nbsp;Susanta Mahapatra","doi":"10.1002/jcc.70171","DOIUrl":"https://doi.org/10.1002/jcc.70171","url":null,"abstract":"<div>\u0000 \u0000 <p>Formamide is the simplest amide, consists of one amide bond, and is an active precursor in prebiotic chemistry. Extensive ab initio calculations have been carried out for the first four electronic and a vibronic coupling Hamiltonian is constructed through the standard vibronic coupling approach, and nuclear dynamics is studied by quantum dynamical methods. Symmetry selection rules are employed, and a 4 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> 4 vibronic Hamiltonian is developed in a diabatic electronic basis. The electronic Hamiltonian elements are expanded using Taylor expansion in terms of normal displacement coordinates (Q <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mi>i</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_i $$</annotation>\u0000 </semantics></math>) of vibrational modes. Both time-independent and time-dependent quantum mechanical methods are utilized in performing nuclear dynamics calculations. The computed and assigned vibronic spectrum is compared with the available experimental data. Time-dependent internal conversion population dynamics is studied to examine the effect of various nonadiabatic couplings in nuclear dynamics.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Study on the Kinetics and Mechanism of Mn-PNP Pincer Complex Catalyzed Addition Reaction Between Benzyl Cyanide and Cinnamonitrile","authors":"Visuwesh Muthukumar,&nbsp;Debdutta Chakraborty","doi":"10.1002/jcc.70209","DOIUrl":"https://doi.org/10.1002/jcc.70209","url":null,"abstract":"<div>\u0000 \u0000 <p>Metal ligand cooperation (MLC) is a catalyst design technique where both the metal center and the ligand framework aid in the binding of the reactant. Recently, a first-row transition metal Mn-PNP pincer complex that works via an aromatisation–dearomatisation mechanism of MLC was reported for base-free dinitrile coupling. We perform microkinetic modeling of the pertinent reaction considering competitive binding of the reactants and branching pathways in the overall scheme. The experimentally reported product yield could be well reproduced by our proposed kinetic scheme. Our results suggest that the presence of water in the system is inhibitive and does not help with the tautomerism of the bound reactant, unlike the previous experimental results for an aliphatic nitrile. We analyze the transition states and intermediates involved in the competitive binding and branching pathways by Fukui function analysis and energy decomposition analysis to rationalize the computed reaction path. The underlying factors that influence the preference of one pathway or the other could be reasonably rationalized based on these analyses.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Lewis Acidity and Lewis Basicity: A Density-Based Reactivity Theory Study","authors":"Lian Zhuo,&nbsp;Yaqin Zheng,&nbsp;Lei Zeng,&nbsp;Yilin Zhao,&nbsp;Meng Li,&nbsp;Chunying Rong,&nbsp;Shubin Liu","doi":"10.1002/jcc.70212","DOIUrl":"https://doi.org/10.1002/jcc.70212","url":null,"abstract":"<div>\u0000 \u0000 <p>Lewis acidity and basicity are among the most widely applied concepts across chemistry, biology, and related disciplines. Yet, their accurate calculation and prediction remain challenging. In this study, we employ descriptors derived from density-based reactivity theory to offer a new and quantitative perspective. To this end, we analyzed four series of Lewis acids and bases across two types of reactions. Our results demonstrate that Lewis acidity and basicity can be effectively quantified using a range of global and local descriptors from conceptual density functional theory and an information-theoretic approach in density functional theory. Additionally, various electronic properties, including frontier molecular orbitals, molecular electrostatic potential, natural valence atomic orbital energies, and several types of atomic charges, were identified as robust descriptors. Leveraging these features, we constructed machine-learning models capable of accurately predicting Lewis acidity and basicity. We also uncovered a strong correlation between Lewis acidity/basicity and electrophilicity/nucleophilicity, further bridging these conceptual frameworks. The consistent high correlations obtained across descriptors, coupled with the performance of our machine learning models, confirm that Lewis acidity and Lewis basicity can be quantitatively characterized with high fidelity. This work suggests that density-based frameworks could provide a powerful and novel foundation for understanding the hard and soft acids and bases principle.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Bonding Evolution Theory Study of the [3+2] Cycloaddition Reaction Between Benzonitrile Oxide and Ethylenic Derivative”","authors":"","doi":"10.1002/jcc.70205","DOIUrl":"https://doi.org/10.1002/jcc.70205","url":null,"abstract":"<p>A. Abel Idrice et al., <i>“</i>Bonding Evolution Theory Study of the [3+2] Cycloaddition Reaction Between Benzonitrile Oxide and Ethylenic Derivative,” <i>Journal of Computational Chemistry</i> 46 (2025): e70164, https://doi.org/10.1002/jcc.70164.</p><p>The error was in the way the names of three of the authors appeared in the published version. The names appeared as Andrés Juan, Olivia Mónica, and Safont Vicent S., and this is incorrect.</p><p>The correct way in which the names should appear is Juan Andrés, Mónica Oliva, and Vicent S. Safont. The online version of this article has been corrected accordingly.</p><p>We apologize for this error.</p>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcc.70205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144807529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BenchQC: A Benchmarking Toolkit for Quantum Computation","authors":"Nia Pollard,&nbsp;Kamal Choudhary","doi":"10.1002/jcc.70202","DOIUrl":"https://doi.org/10.1002/jcc.70202","url":null,"abstract":"<div>\u0000 \u0000 <p>The Variational Quantum Eigensolver (VQE) is a widely studied hybrid classical-quantum algorithm for approximating ground-state energies in molecular and materials systems. This study benchmarks the performance of the VQE for calculating ground-state energies of small aluminum clusters (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mtext>Al</mtext>\u0000 <msup>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{Al}}^{-} $$</annotation>\u0000 </semantics></math>, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mtext>Al</mtext>\u0000 <msub>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{Al}}_2 $$</annotation>\u0000 </semantics></math>, and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mtext>Al</mtext>\u0000 <msubsup>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 </mrow>\u0000 </msubsup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{Al}}_3^{-} $$</annotation>\u0000 </semantics></math>) within a quantum-density functional theory (DFT) embedding framework, systematically varying key parameters: (I) classical optimizers, (II) circuit types, (III) number of repetitions, (IV) simulator types, (V) basis sets, and (VI) noise models. All calculations were performed using quantum simulators to evaluate VQE performance under both idealized and noise-augmented conditions. Our findings demonstrate that certain optimizers converge efficiently, while circuit choice and basis set selection have a marked impact on energy estimates, with higher-level basis sets closely matching classical computation data from Numerical Python Solver (NumPy) and Computational Chemistry Comparison and Benchmark DataBase (CCCBDB). To approximate realistic conditions, we employed IBM noise models to simulate the effects of hardware noise. The results showed close agreement with CCCBDB benchmarks, with percent errors consistently below 0.2%. The results demonstrate that VQE can approximate energy estimates under simulated conditions for small aluminum clusters and highlight the importance of optimizing quantum-DFT parameters to balance computational cost and precision. This work contributes to ongoing efforts to benchmark VQE in practical settings and lays the groundwork for future benchmarking tools for quantum chemistry and materials applications.</p>\u0000 </div>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 21","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}