Journal of Chemical Theory and Computation最新文献

Understanding Electronic Excitations Between Single Determinants with Occupied-Virtual Orbitals for Chemical Valence. 理解化学价态中具有占据虚轨道的单一决定因素之间的电子激发。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-15 DOI: 10.1021/acs.jctc.5c01029

Hengyuan Shen,Nicola Bogo,Christopher J Stein,Martin Head-Gordon

{"title":"Understanding Electronic Excitations Between Single Determinants with Occupied-Virtual Orbitals for Chemical Valence.","authors":"Hengyuan Shen,Nicola Bogo,Christopher J Stein,Martin Head-Gordon","doi":"10.1021/acs.jctc.5c01029","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01029","url":null,"abstract":"One approach to calculating electronic excited states treats both ground and excited states as single determinants, either by direct optimization or with the aid of constraints. In this work, we extend the theory of occupied-virtual orbitals for chemical valence (OVOCV) to analyze the orbital character of excitations computed in this way. An intermediate frozen state that is polarization-free is introduced to cleanly separate the primary excitation from the accompanying orbital relaxation of spectator orbitals. A variety of chemical examples are reported using the OVOCV excitation analysis on orbital-optimized density functional theory (OO-DFT) calculations, including charge-transfer excitations, core excitations and singly and doubly excited valence states. Orbital relaxation effects are typically collective, and can be as large as 4-5 eV (with roughly 0.1 e- promoted) in charge transfer states, and even larger in core excited states. OVOCV analysis differs from natural transition orbital (NTO) analysis; we show that direct use of NTOs can largely obscure the role of orbital relaxation in favor of the primary excitation.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"36 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels. 主宰一切：跨越量子化学水平的普遍原子间势能学习。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-12 DOI: 10.1021/acs.jctc.5c00858

Yuxinxin Chen, Pavlo O Dral

{"title":"One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels.","authors":"Yuxinxin Chen, Pavlo O Dral","doi":"10.1021/acs.jctc.5c00858","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00858","url":null,"abstract":"With the development of universal machine learning interatomic potentials, a rapidly growing number of chemical space data sets have appeared. One of the biggest challenges is that these data sets are mostly generated at different quantum chemical (QC) levels. However, a general framework that is scalable to learning across both the chemical space and quantum chemical levels remains unmet. In this work, we propose an all-in-one approach that enables simultaneous learning on an arbitrary number of QC levels from various data sets, presenting a more general and easier-to-use alternative to transfer learning. We showcase the superiority of our all-in-one strategy by creating OMNI-P1─the first-ever universal interatomic potential capable of simultaneously learning and making predictions at different QC levels. The generalization capability of the universal model OMNI-P1 for organic molecules is comparable to semiempirical GFN2-xTB and common density functional theory (DFT) methods with a double-ζ basis set, while its speed is orders of magnitude faster. Due to its unique ability to make predictions at different levels, a single model trained with our approach provides a straightforward way to also generate correction terms. This can be used in Δ-learning models without the need to train a dedicated correction model. We utilized this capability of OMNI-P1 to correct the DFT ωB97X-D4 level to obtain the Ω-ωB97X-D4 method with superior accuracy.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytic Gradients and Periodic Boundary Conditions for Direct Reaction Field Polarizable QM/MM with Electrostatic Potential Fitting. 静电势拟合直接反应场极化QM/MM的解析梯度和周期边界条件。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-12 DOI: 10.1021/acs.jctc.5c00863

Thomas P Fay, Miquel Huix-Rotllant, Nicolas Ferré

{"title":"Analytic Gradients and Periodic Boundary Conditions for Direct Reaction Field Polarizable QM/MM with Electrostatic Potential Fitting.","authors":"Thomas P Fay, Miquel Huix-Rotllant, Nicolas Ferré","doi":"10.1021/acs.jctc.5c00863","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00863","url":null,"abstract":"Our recently developed Direct Reaction field with ESPF Embedding Model (DREEM) method offers an efficient and physically rigorous framework for incorporating polarizable molecular mechanics (MM) environments into quantum mechanics/molecular mechanics (QM/MM) simulations. By coupling the QM and MM regions through the instantaneous MM electrostatic polarization response to QM charge density fluctuations, DREEM enables consistent treatment of ground and excited electronic states, capturing electronic state-specific polarization and dispersion effects absent in conventional mean-field or linear response approaches. The use of the electrostatic potential fitting (ESPF) approximation method to describe charge density fluctuations significantly improves the computational efficiency compared to the integral-exact direct reaction field. In this work, we present two methodological advancements to extend the applicability of DREEM to realistic condensed-phase simulations: first, the development of efficient analytic energy gradients, enabling geometry optimization, transition state searches, and molecular dynamics; and second, a formulation of periodic boundary conditions (PBC) compatible with the DREEM framework. These capabilities are implemented in the open-source OpenESPF code, interfacing PySCF and OpenMM for high-performance QM and MM calculations. We demonstrate that the resulting implementation enables practical simulations of excited-state optical properties in periodic polarizable environments, where we calculate the fluorescence spectrum of acetone in water, including quantum vibronic and non-Condon effects. This paves the way for predictive modeling of photochemical reactivity and spectroscopy in complex systems where environment polarization is important.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Roadmap to CCSD(T)-Quality Machine-Learned Potentials for Condensed Phase Simulations. CCSD(T)的路线图-浓缩相模拟的质量机器学习势。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-12 DOI: 10.1021/acs.jctc.5c01085

Eric D Boittier,Silvan Käser,Markus Meuwly

{"title":"Roadmap to CCSD(T)-Quality Machine-Learned Potentials for Condensed Phase Simulations.","authors":"Eric D Boittier,Silvan Käser,Markus Meuwly","doi":"10.1021/acs.jctc.5c01085","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01085","url":null,"abstract":"Accurate, yet computationally efficient energy functions are essential for state-of-the-art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow based on a combination of machine-learning-based and empirical representations of intra- and intermolecular interactions is presented. The total energy is decomposed into internal contributions and electrostatic and van der Waals interactions between monomers. The monomer potential energy surface is described using a neural network, whereas for the electrostatics the flexible minimally distributed charge model is employed. Remaining contributions between reference energies from electronic structure calculations and the model are fitted to standard Lennard-Jones (12-6) terms. For water as a topical example, reference energies for the monomers are determined from CCSD(T)-F12 calculations, whereas for an ensemble of cluster structures containing [2, 60] and [2, 4] monomers DFT and CCSD(T) energies, respectively, were used to adjust the van der Waals contributions. Based on the bulk liquid density and heat of vaporization, the best-performing set of LJ(12-6) parameters was selected and a wide range of condensed phase properties were determined and compared with experiment. MD simulations on the multiple-nanosecond time scale were carried out for water boxes containing 2000 to 8000 monomers, depending on the property considered. The performance of such a generic ML-inspired parametrization scheme is very promising and future improvements and extensions are discussed, also in view of recent advances for water in particular in the literature.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"35 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Reliability of Truncated Coupled Cluster Wave Function: Estimating the Distance from the Exact Solution. 截断耦合聚类波函数的可靠性评估：与精确解的距离估计。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-12 DOI: 10.1021/acs.jctc.5c00750

Ádám Ganyecz, Zsolt Benedek, Klára Petrov, Gergely Barcza, András Olasz, Miklós A Werner, Örs Legeza

{"title":"Assessing the Reliability of Truncated Coupled Cluster Wave Function: Estimating the Distance from the Exact Solution.","authors":"Ádám Ganyecz, Zsolt Benedek, Klára Petrov, Gergely Barcza, András Olasz, Miklós A Werner, Örs Legeza","doi":"10.1021/acs.jctc.5c00750","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00750","url":null,"abstract":"A new approach is proposed to assess the reliability of the truncated wave function methods by estimating the deviation from the full configuration interaction (FCI) wave function. While typical multireference diagnostics compare some derived property of the solution with the ideal picture of a single determinant, we try to answer a more practical question: how far is the solution from the exact one. Using the density matrix renormalization group (DMRG) method to provide an approximate FCI solution for the self-consistently determined relevant active space, we compare the low-level CI expansions and one-body reduced density matrixes to determine the distance of the two solutions (d̃Φ, d̃γ). We demonstrate the applicability of the approach for the CCSD method by benchmarking on the W4-17 data set, as well as on transition-metal-containing species. We also show that the presented moderate-cost, purely wave function-based metric is truly unique in the sense that it does not correlate with any popular multireference measures. We also explored the usage of CCSD natural orbitals (d̃γ,NO) and its effect on the active space size and the metric. The proposed diagnostic can also be applied to other wave function approximations, and it has the potential to provide a quality measure for post-Hartree-Fock procedures in general.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CYCLO70: A New Challenging Pericyclic Benchmarking Set for Kinetics and Thermochemistry Evaluation. CYCLO70：一个新的具有挑战性的动力学和热化学评价的周环基准集。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-11 DOI: 10.1021/acs.jctc.5c00925

Javier E Alfonso-Ramos, Carlo Adamo, Éric Brémond, Thijs Stuyver

{"title":"CYCLO70: A New Challenging Pericyclic Benchmarking Set for Kinetics and Thermochemistry Evaluation.","authors":"Javier E Alfonso-Ramos, Carlo Adamo, Éric Brémond, Thijs Stuyver","doi":"10.1021/acs.jctc.5c00925","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00925","url":null,"abstract":"Here, a new challenging benchmarking data set for cycloaddition reactions, CYCLO70, is presented and analyzed. CYCLO70 has been generated with the specific aim of being representative of the most challenging regions of the chemical reaction space surrounding Diels-Alder, dipolar cycloadditions, and (sigmatropic) rearrangement reactions with the help of an active learning approach. Testing 93 different functionals, spanning from spin-local density approximation to the most recent double-hybrid functionals, we observe that the errors on CYCLO70 are significantly bigger than those on the cycloaddition subset of BH9, the most popular benchmarking data set for this reaction class. Furthermore, we observe that the range-separated hybrid ωB97M-V is the best performing functional to model barrier heights and reaction energies, with a deviation closest to the desirable \"chemical accuracy\"; among the double hybrids, PBE-QIDH performs best, and among the fixed-range hybrids, M06-2X and r2SCAN0 emerge as the most balanced in terms of simultaneously reproducing both properties. Next, we perform a principal component analysis on the errors across the data set and demonstrate not only that the errors across different functional approximations correlate to a significant extent (the first two components explain 98% of the variance), but we also observe that functionals belonging to the same rung of Jacob's ladder cluster together in the constructed two-dimensional plot. These results were further validated on a set of Diels-Alder reactions relevant to self-healing polymer design, reinforcing the practical relevance of CYCLO70.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Uncovers the Rapid Activation of Catch-Bonds under Force. AI揭示了武力下捕获键的快速激活。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-11 DOI: 10.1021/acs.jctc.5c01181

Marcelo C R Melo,Rafael C Bernardi

{"title":"AI Uncovers the Rapid Activation of Catch-Bonds under Force.","authors":"Marcelo C R Melo,Rafael C Bernardi","doi":"10.1021/acs.jctc.5c01181","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01181","url":null,"abstract":"Mechanically resilient protein interactions are crucial for biological processes ranging from bacterial adhesion to human tissue formation. Catch-bonds, a unique class of protein interactions that strengthen under force, act like a molecular finger trap, tightening to prevent bond rupture. However, it remains unclear whether catch-bonds form immediately upon force application or require a specific force threshold for stabilization. Here, we employ an in silico single-molecule force spectroscopy approach that combines molecular dynamics (MD) simulations, dynamical network analysis, and AI-based modeling to investigate the XDoc:CohE complex, a hyperstable catch-bond found in cellulose-degrading bacteria. By analyzing amino acid interactions between XDoc and cohesin E, and between XDoc submodules (X-module and Doc), we show that AI regression models can accurately predict rupture forces using only short MD simulations, capturing key mechanostability features despite the binding interface's complexity. Our results reveal that mechanostability signatures emerge early under force load, indicating that catch-bonds activate almost immediately. These findings provide new insights into the molecular principles governing force-dependent protein interactions and highlight the potential of AI-driven approaches for predicting and characterizing mechanostability, with broad implications for bioengineering and drug design.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"35 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perspective: Vibronic Coupling Potentials for Trajectory-Based Excited-State Dynamics. 展望：基于轨迹的激发态动力学的振动耦合势。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-11 DOI: 10.1021/acs.jctc.5c01002

Sandra Gómez,Patricia Vindel-Zandbergen,Dilara Farkhutdinova,Leticia González

{"title":"Perspective: Vibronic Coupling Potentials for Trajectory-Based Excited-State Dynamics.","authors":"Sandra Gómez,Patricia Vindel-Zandbergen,Dilara Farkhutdinova,Leticia González","doi":"10.1021/acs.jctc.5c01002","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01002","url":null,"abstract":"This Perspective reviews the use of vibronic coupling (VC) potentials in trajectory-based excited-state dynamics simulations. Originally developed to provide simplified yet physically grounded representations of nonadiabatic interactions, VC models─particularly their linear version (LVC)─have facilitated extensive investigations of photophysical and photochemical processes, in both molecular and condensed-phase systems. By effectively capturing the coupling between electronic and vibrational motions, VC models enable efficient dynamical simulations, making it feasible to investigate larger and more complex systems, for longer time scales or relying on potential energy surfaces calculated with high levels of theory. These models provide valuable insights into energy and charge transfer mechanisms following photoexcitation, shedding light on excited-state lifetimes and intricate relaxation pathways. Here, we discuss their integration with three trajectory-based computational families of methods: surface hopping, variational multiconfigurational Gaussian, and exact-factorization-derived approaches. We showcase how VC models have helped uncovering key mechanistic insights, including state-specific intersystem crossing pathways and vibrational mode selectivity. As the field progresses, VC-based approaches are expected to be increasingly combined with machine learning, anharmonic corrections, and hybrid LVC/MM frameworks, broadening their applicability to complex, flexible, and solvated environments. We highlight the advantages of VC-based potentials for trajectory-based simulations, emphasizing their computational efficiency and usefulness for benchmarking and exploring photophysical processes in molecular systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation. 分子：用于熵正则化分类和不确定性感知配体评估的分子动力学和优化深度学习。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-11 DOI: 10.1021/acs.jctc.5c01140

Ivan Cucchi,Elena Frasnetti,Francesco Frigerio,Fabrizio Cinquini,Silvia Pavoni,Luca F Pavarino,Giorgio Colombo

{"title":"MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation.","authors":"Ivan Cucchi,Elena Frasnetti,Francesco Frigerio,Fabrizio Cinquini,Silvia Pavoni,Luca F Pavarino,Giorgio Colombo","doi":"10.1021/acs.jctc.5c01140","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01140","url":null,"abstract":"Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric. Extending this approach, we curated a wide and diverse kinase data set (comprising 280 experimentally resolved structures) to train and evaluate a new dual-modal deep neural network classifier, which is tailored to process separately and efficiently the dynamical and structural data to predict the mode of action of a compound. The developed model demonstrated robust classification performance, effective uncertainty handling, and underscored the critical importance of incorporating protein dynamics data. Remarkably, our method maintained high performance even with imputed dynamics data, enabling rapid compound screening and prioritization, without the need for extensive MD simulations.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating Metal Complex Formation and Ligand Exchange: Unraveling the Interplay between Entropy, Kinetics, and Mechanisms on the Chelate Effect. 模拟金属配合物的形成和配体交换：揭示熵、动力学和螯合效应机制之间的相互作用。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-09-11 DOI: 10.1021/acs.jctc.5c01079

Luca Sagresti,Luca Benedetti,Kenneth M Merz,Giuseppe Brancato

{"title":"Simulating Metal Complex Formation and Ligand Exchange: Unraveling the Interplay between Entropy, Kinetics, and Mechanisms on the Chelate Effect.","authors":"Luca Sagresti,Luca Benedetti,Kenneth M Merz,Giuseppe Brancato","doi":"10.1021/acs.jctc.5c01079","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01079","url":null,"abstract":"Metal coordination is ubiquitous in Nature and central in many applications, ranging from nanotechnology to catalysis and environmental chemistry. Complex formation results from the subtle interplay between different thermodynamic, kinetic, and mechanistic contributions, which remain largely elusive to standard experimental methodologies and challenging for typical modeling approaches. Here, considering some prototypical metal complexes between Cd(II) and Ni(II) with various amine ligands, we present a comprehensive atomistic-level description of their chemical equilibrium, complex formation, and ligand exchange dynamics in aqueous solution, providing an excellent agreement with available association constants and formation rates spanning several orders of magnitude. This is achieved through an effective molecular simulation approach that combines finely tuned interatomic potentials with state-of-the-art enhanced sampling and kinetics techniques. Worthy of note, the nature of the chelate effect, a fundamental concept in coordination chemistry, is fully unravelled through the comparative analysis of the ligand binding reactions of monodentate and bidentate ligands in octahedral complexes. Results provide a complete picture illustrating all the concurrent contributions to this phenomenon, such as entropy, dissociation rates, and ligand binding mechanisms, in some cases contradicting previously held beliefs. This study represents a step forward for the in silico design and applications of coordination complex systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"67 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0