Journal of Chemical Theory and Computation最新文献

{"title":"A Thermodynamically\u0000Consistent Approach to Molecular\u0000Simulations of Adsorption-Induced Deformation and Structural Transitions\u0000in MOFs","authors":"Nicholas J. Corrente,Kaelyn Chang,Muhtasim Noor,Alexander V. Neimark","doi":"10.1021/acs.jctc.6c01243","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01243","url":null,"abstract":"Abstract Flexible metal–organic frameworks (MOFs) exhibit coupled adsorption-deformation behavior that cannot be captured in molecular detail by conventional rigid-framework simulations. We present an iterative hybrid GCMC/MD methodology that explicitly couples grand canonical Monte Carlo sampling of adsorbate configurations with isothermal–isobaric molecular dynamics relaxation of the framework structure, using a Metropolis acceptance criterion in the osmotic ensemble to ensure thermodynamic consistency. Applied to argon adsorption on ZIF-8 at 87.3 K, the method quantitatively reproduces the experimental stepped isotherm characteristic of the gate-opening transition between low pressure (LP) and high pressure (HP) conformations and predicts nonmonotonic strain isotherm with initial contraction (∼0.5%) at low loadings followed by expansion (∼0.7%) during gate-opening. In addition, the elastic modulus variation upon loading is calculated from the volume fluctuations. The simulations reveal and quantify the molecular mechanism of gate-opening through cooperative linker reorientation from a unimodal swing angle distribution centered at 0° to a bimodal distribution peaked at ± 23°. The proposed approach is computationally efficient, yielding converged strain and compressibility isotherms within an accessible number of iterations, and provides a general framework for predicting adsorption-induced structural transitions in flexible porous materials without a priori knowledge of end point structures.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"4 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091990","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}

{"title":"Enhancing the Efficiency\u0000and Flexibility of AutoMeKin:\u0000Integrating ORCA and Machine-Learning Potentials","authors":"Omar Rodríguez-López,Emilio Martínez-Núñez,Berta Fernández,Saulo A. Vázquez","doi":"10.1021/acs.jctc.6c01565","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01565","url":null,"abstract":"Abstract Automated reaction discovery is often limited by the cost of high-level refinement of large reaction networks. Here, we extend AutoMeKin, an automated reaction-discovery program, by presenting a complete integration of ORCA 6 into its high-level refinement workflow, providing access to a broad range of electronic-structure methods through a package freely available to academic users. We also integrate the UMA and MACE-OMOL-0 machine-learning interatomic potentials and Δ-ML energy corrections, enabling automated refinement of reaction networks using conventional electronic-structure methods, foundation ML potentials, or ML-corrected quantum-mechanical approaches within a unified framework. The workflow is assessed for five systems, monoethanolamine, three cyclobutadiene-derived radicals, and N-[bis(2H-triazol-4-yl)methylidene]hydroxylamine, spanning both in-distribution and challenging out-of-distribution chemistry. Comparisons with reference data show that UMA and MACE-OMOL-0 generally recover the relevant reaction mechanisms and provide energy profiles of comparable quality to DFT at substantially reduced cost. RRKM analyses further reveal that vibrational frequencies can significantly affect product population fractions, especially for loose transition states. These results show that the new implementation increases the accessibility and flexibility of AutoMeKin and further support the view that foundation ML potentials can serve as practical surrogates for DFT in automated reaction-network discovery.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"24 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091891","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}

{"title":"Dual Machine-Learning\u0000Prediction of Excited-State\u0000Energetics from Ground-State Descriptors and Its Implication on Intramolecular\u0000Singlet Fission in Dimeric Chromophores","authors":"Hongyang Wang,Wenjing Fan,Xiaoqing Zhang,Xiufang Song,Xinyu Song,Jifan Gao,Shibo Cheng,Yuxiang Bu","doi":"10.1021/acs.jctc.6c01469","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01469","url":null,"abstract":"Abstract Accurately describing excited-state (ES) properties in large condensed-phase systems remains challenging due to structural flexibility and the limited scalability of quantum methods, both crucial for understanding photo-functional processes such as singlet fission (SF). Traditional approaches often struggle to link macroscopic observations with transient, configuration-specific ES dynamics and ensemble behavior in fluctuating environments. To bridge this gap, we introduce a dual machine learning (ML) framework: first, an ML potential trained on AIMD data enables efficient molecular dynamics sampling (MLMD); second, a novel physics-informed ML model (GD-ML) predicts vertical excitation energies with high accuracy using only ground-state descriptors (GDs) related to electronic properties. This framework is computationally far more efficient than performing large-scale TDDFT calculations directly, achieving approximately 56% acceleration. Applied to a tetracene dimer in solution, the framework reveals that toluene and DMF exhibit similar probabilistic distributions of the thermodynamic SF driving force (ΔESF). However, toluene favors conformations with localized molecular orbitals (MOs), opening a kinetic channel that enhances LE/CT state coupling and facilitates SF. In contrast, DMF stabilizes conformations with delocalized MOs, suppressing this channel. These insights offer a mechanistic explanation for the experimentally observed solvent dependence of SF efficiency. Key results underscore how symmetric homodimer heterogenization promotes SF by modulating MO delocalization and charge-transfer contributions to Coulomb/exchange energies, thereby influencing excitation character. Ensemble analysis further informs solvent selection for tailored photoexcitation performance. This work establishes a transformative paradigm for high-throughput exploration of photo-functional processes in complex molecular systems by correlating ES properties with ground-state electronic descriptors.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"147 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091890","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}

{"title":"First-Principles Modeling of Charge Transfer Excitons\u0000in Organic Solar Cells","authors":"Vishal Jindal,Michael J. Janik,Scott T. Milner","doi":"10.1021/acs.jctc.6c01456","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01456","url":null,"abstract":"Abstract Charge-transfer (CT) excitons formed at donor–acceptor interfaces play a central role in charge separation in organic solar cells. Their binding energy and spatial structure are highly sensitive to local molecular geometry, particularly in disordered morphologies. In this work, we present a multiscale computational framework to model CT excitons at the interface between poly(3-hexylthiophene) (P3HT) and the nonfullerene acceptor O-IDTBR, a representative polymer-nonfullerene blend. We extend a previously developed tight-binding model to include kinetic energy, Coulomb interaction and dielectric polarization, with all parameters derived from density functional theory (DFT). Using molecular dynamics simulations of an amorphous P3HT:IDTBR blend, we identify hundreds of donor–acceptor contact geometries and compute the CT exciton for each by minimizing the total energy with respect to electron and hole wavefunctions. We analyze the distribution of CT exciton energies, compare them with localized excitons on IDTBR, and estimate binding energies relative to free polarons in the bulk phases. By decomposing the CT energy into one-body kinetic and two-body (Coulomb + polarization) contributions, we show that the primary variation of CT energies is controlled by the underlying polaron energy landscape, while configuration-dependent Coulomb and polarization stabilization explains the residual scatter and distinguishes true CT states from quasi-separated polarons. Additionally, we place these results in the context of a full energy landscape from HOMO–LUMO gaps to excitons, CT states, and polarons, highlighting how interfacial structure tunes the balance between exciton formation and dissociation. This approach enables predictive evaluation with no adjustable parameters of CT excitons across realistic morphologies, and offers design principles for engineering interfaces that promote efficient charge separation in organic photovoltaics.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091930","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}

{"title":"Accessing Free-Energy Barriers from Unbiasing Dynamics:\u0000Delocalized Transition States in Heterogeneous Catalysis","authors":"Peipei Zhang,Chenxi Guo,P. Hu","doi":"10.1021/acs.jctc.6c01132","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01132","url":null,"abstract":"Abstract Accurate determination of free-energy barriers remains a central challenge in heterogeneous catalysis. Conventional approaches rely on static potential-energy surfaces or biased molecular dynamics, which often underestimate entropic contributions and distort the true transition-state character. Here we introduce Screened Standard Molecular Dynamics (SSMD), an unbiased computational strategy that initiates standard MD trajectories from quasi-transition states to enrich sampling in the delocalized transition region without artificial constraints or biasing potentials. Reaction free-energy barriers are subsequently extracted from these trajectories through confidence-interval force screening followed by mean-force integration. Benchmarking on CO oxidation and ethane dehydrogenation over Pt(111), we find that SSMD yields smooth, converged free-energy profiles at an order of magnitude lower computational cost than constrained methods. SSMD systematically predicts slightly lower barriers than biased approaches, with deviations increasing at elevated temperatures. We show that the transition state is more delocalized than previously thought, a feature SSMD uniquely captures but conventional schemes severely underestimate, accounting for the observed barrier differences. It is worth noting that SSMD requires the projection of forces onto an appropriate reaction coordinate, which necessitates data analysis and processing and may therefore introduce some degree of bias. The convergence and sensitivity tests presented demonstrate the numerical robustness of these choices for the systems studied.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"11 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091892","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}

{"title":"Unified Mesoscale Framework Bridging Equilibrium Thermodynamics\u0000and Transport Properties of Ionic Liquids and Their Mixtures","authors":"Young Jin Lee,Qin M. Qi","doi":"10.1021/acs.jctc.6c01130","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01130","url":null,"abstract":"Abstract Ionic liquids (ILs) have emerged as sustainable alternatives to molecular solvents because of their unique physicochemical properties. Although quantum chemical simulations provide fundamental insights into their intermolecular interactions, constructing coarse-grained models that preserve equilibrium thermodynamics while quantitatively reproducing the prescribed transport properties remains challenging. This simulation challenge comes primarily from the high viscosity and low diffusivity of ILs due to strong ion pairing, resulting in Schmidt numbers (Sc) that span multiple orders of magnitude. In this work, we present a unified mesoscale simulation framework that preserves thermodynamic consistency while quantitatively reproducing the transport properties of pure ILs and their mixtures. This framework integrates a bottom-up approach and an extended dissipative particle dynamics (DPD) formulation: equilibrium thermodynamics are derived from COSMO-RS analysis and fragment molecular orbital (FMO) calculations, while DPD resolves coarse-grained dissipative forces into parallel and perpendicular components. A key contribution of this study is a micromechanically grounded algorithm that systematically determines the dynamic parameters from constitutive correlations. Using pair correlation functions from bottom-up-parametrized equilibrium structures, two preliminary simulations establish the reference constitutive correlations, which are then used within the composition-dependent calibration procedure to match experimental transport properties across varying solvent compositions. We validate this framework using an imidazolium-based IL system, which demonstrates quantitative agreement with experimental transport properties and captures negative viscosity deviations from ideal mixing. Our framework bridges molecular chemistry and macroscopic rheology and will facilitate the screening, design, and implementation of IL-based processes for energy- and separation-related systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"66 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091931","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}

{"title":"Convolutional Kernel-Embedded\u0000 E\u0000 (3)-Equivariant\u0000Networks (KEN): Overcoming Architectural Rigidity in Machine Learning\u0000Interatomic Potentials","authors":"Jha Gautam","doi":"10.1021/acs.jctc.6c01109","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01109","url":null,"abstract":"Abstract Machine learning interatomic potentials (MLIPs) have emerged as scalable alternatives to first-principles methods such as density functional theory (DFT). Among them, E(3)-equivariant graph neural networks (GNNs) like PACE and MACE are highly accurate but structurally rigid, limiting the incorporation of rich, multichannel electronic descriptors. We introduce convolutional kernel-embedded E(3)-equivariant networks (KEN), a hybrid architecture that combines the local environmental extraction of 2D convolutional kernels with the many-body expressivity of an E(3)-equivariant GNN backbone. This design preserves symmetry while enabling flexible, multimodal input features without increasing training data requirements. Trained and tested on heavy element 26 (HE26) dataset, KEN was compared with MACE-osaka26 model. Further validation on lattice parameter prediction through Birch–Murnaghan equation of state, magnetic moment prediction, and comparison with CHGNet framework, and microcanonical molecular dynamics confirms robust energy conservation and accurate reproduction of the forces, and thus, demonstrates a viable pathway to overcoming architectural rigidity in state-of-the-art MLIPs.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"18 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091933","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}

{"title":"Beyond Multiscale: A Neural-Network-Unified Molecular\u0000Model for Asymmetry-Free Dynamics Simulation","authors":"Takuma Ikeda,Hiroshi C. Watanabe,Haruyuki Nakano","doi":"10.1021/acs.jctc.6c01037","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01037","url":null,"abstract":"Abstract Adaptive multiscale simulations, such as adaptive quantum mechanics (QM)/molecular mechanics (MM), are indispensable for investigating complex solution dynamics but historically suffer from a fundamental dilemma: the dynamic exchange of identical solvent molecules across different model resolutions introduces an energetic asymmetry under solvent exchange (specifically, a permutation asymmetry where physically identical molecules are treated indistinguishably yet subjected to different force fields depending on their instantaneous positions). This asymmetry forces a prohibitive trade-off between severe structural distortions and the violation of Hamiltonian conservation (continuous energy drift). To resolve this issue, we propose the “Integrated Molecular Model,” a paradigm-shifting framework that unifies multiple resolutions into a single, dynamically consistent potential. By employing a neural network that takes only the multiscale weight functions as input descriptors, our model learns to systematically cancel the nonphysical transition forces originating from spatial discontinuities. As a proof-of-concept, we validated this unified framework on bulk water, aqueous ionic solutions, and reactive hydronium ions. Our approach not only faithfully restores artifact-free solvation structures but also strictly conserves the Hamiltonian, enabling stable, long-term simulations under the microcanonical (NVE) ensemble. Consequently, the model successfully eliminates artificial thermostat-induced breathing modes, allowing for the rigorous evaluation of intrinsic dynamical properties such as diffusion coefficients. Furthermore, owing to its physically informed network design, the correction potential introduces less than 2% additional computational overhead and demonstrates remarkable zero-shot transferability across chemically similar ionic species. By eradicating the inherent asymmetry of adaptive multiscale modeling, this highly scalable framework establishes a robust foundation for high-fidelity simulations of complex condensed-phase phenomena, including reactive dynamics at solid–liquid interfaces and ion transport in battery electrolytes.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091988","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}

{"title":"ViBra: Configuration\u0000Interaction for Anharmonic Vibrational\u0000Spectroscopy and Quantum-Sampled Configuration Spaces","authors":"Raphael F. Ligorio,Marco Antonio Barroca,Alan Duriez,Mathias B. Steiner","doi":"10.1021/acs.jctc.6c01564","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01564","url":null,"abstract":"Abstract Quantum-centric workflows are a promising route to improving the accuracy of property predictions in computational chemistry and materials science. By integrating quantum sampling algorithms with classical solvers, electronic structure calculations have recently demonstrated their potential even on noisy intermediate-scale quantum devices. In principle, the method of Vibrational Configuration Interaction (VCI) is suitable for integration with quantum sampling algorithms as well. However, demonstrations of computational workflows for quantum-centric, vibrational property predictions are still lacking. Here, we introduce a methodology for performing anharmonic vibrational structure calculations that can be deployed in a hybrid, quantum-classical mode. Starting from a quartic force field, the approach combines a Vibrational Self-Consistent Field (VSCF) with VCI in Full, Selected (S-VCI), or Symmetry-Adapted (SA-VCI) modes. In S-VCI, an Epstein–Nesbet perturbative screening significantly reduces the configuration space while retaining high predictive accuracy. A state-list input enables the integration of externally generated vibrational configurations as a seed space. As a proof-of-concept, we demonstrate a hybrid, quantum-classical computational workflow in which a quantum sampling algorithm provides the seed. Our vibrational wave function analysis package ViBra, equipped with a graphical interface, is available at https://github.com/raphafe96/ViBra.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"41 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090832","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}

{"title":"Correction to “Two-Photon Absorption Strengths of Small Molecules: Reference CC3 Values and Benchmarks”","authors":"Carmelo Naim, Robert Zaleśny, Denis Jacquemin","doi":"10.1021/acs.jctc.6c01763","DOIUrl":"10.1021/acs.jctc.6c01763","url":null,"abstract":"","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 18","pages":"9879–9879"},"PeriodicalIF":5.8,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148858537","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}