Journal of Chemical Theory and Computation最新文献

{"title":"Individual and Cooperative Superexchange Enhancement in Cuprates","authors":"Tonghuan Jiang,&nbsp;, ,&nbsp;Nikolay A. Bogdanov*,&nbsp;, ,&nbsp;Ali Alavi*,&nbsp;, and ,&nbsp;Ji Chen*,&nbsp;","doi":"10.1021/acs.jctc.5c00755","DOIUrl":"10.1021/acs.jctc.5c00755","url":null,"abstract":"<p >It is now widely accepted that the antiferromagnetic coupling within high-temperature superconductors strongly exhibits a profound correlation with the upper limit of the superconducting transition temperature these materials can reach. Thus, accurately calculating the positive and negative mechanisms that influence magnetic coupling in specific materials is crucial for the exploration of superconductivity at higher temperatures. Nevertheless, it is notoriously difficult to establish a complete description of electron correlations employing ab initio theories because of the large number of orbitals involved. In this study, we tackle the challenge of achieving high-level ab initio wave function theory calculations that allow an explicit treatment of electron correlations associated with a large number of high-energy orbitals. We elucidate the atomic-shell-wise contributions to the superexchange coupling in the lanthanum cuprate, including individual effects of high-energy orbitals (Cu 4d, 5d, 4f, 5p) and cooperative effects between the core and these high-energy orbitals. Specifically, the prominent contributions from Cu 4d, 5d, 4f, and 5p give rise to a rich collection of previously unexamined superexchange channels. We propose a <i>p</i>-<i>d</i>-<i>f</i> model to universally account for the contributions of high-energy orbitals at copper sites. Our calculations and physical rationalizations offer a more robust theoretical foundation for investigating cuprate-type high-temperature superconductors.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9364–9375"},"PeriodicalIF":5.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145074152","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":"Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by Nano-GPT","authors":"Wenqi Zeng,&nbsp;, ,&nbsp;Lu Zhang,&nbsp;, and ,&nbsp;Yuan Yao*,&nbsp;","doi":"10.1021/acs.jctc.5c00180","DOIUrl":"10.1021/acs.jctc.5c00180","url":null,"abstract":"<p >Long-term biomolecular dynamics is critical for understanding key evolutionary transformations in molecular systems. However, capturing these processes requires extended simulation timescales that often exceed the practical limits of conventional models. To address this, shorter simulations, initialized with diverse perturbations, are commonly used to sample the phase space and explore a wide range of behaviors. Recent advances have leveraged language models to infer long-term behavior from short trajectories, but methods such as long short-term memory (LSTM) networks are constrained to low-dimensional reaction coordinates, limiting their applicability to complex systems. In this work, we present nano-GPT, a novel deep learning model inspired by the GPT architecture specifically designed to capture long-term dynamics in molecular systems with fine-grained conformational states and complex transitions. The model employs a two-pass training mechanism that incrementally replaces molecular dynamics (MD) tokens with model-generated predictions, effectively mitigating the accumulation errors inherent in the training window. We validate nano-GPT on three distinct systems: a four-state model potential, the alanine dipeptide, a well-studied simple molecule, and the Fip35 WW domain, a complex biomolecular system. Our results show that nano-GPT effectively captures long-time scale dynamics by learning high-order dependencies through an attention mechanism, offering a novel perspective for interpreting biomolecular processes.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9239–9248"},"PeriodicalIF":5.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.5c00180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145074090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expressivity of Determinantal Ansatzes for Neural Network Wave Functions","authors":"Ni Zhan,&nbsp;, ,&nbsp;William A. Wheeler,&nbsp;, ,&nbsp;Gil Goldshlager,&nbsp;, ,&nbsp;Elif Ertekin,&nbsp;, ,&nbsp;Ryan P. Adams,&nbsp;, and ,&nbsp;Lucas K. Wagner*,&nbsp;","doi":"10.1021/acs.jctc.5c01243","DOIUrl":"10.1021/acs.jctc.5c01243","url":null,"abstract":"<p >Neural network wave functions have shown promise as a way to achieve high accuracy in solving the many-body quantum problem. These wave functions most commonly use a determinant or a sum of determinants to antisymmetrize many-body orbitals, which are described by a neural network. In many cases, the wave function is projected onto a fixed-spin state. Such a treatment is allowed for spin-independent operators; however, it cannot be applied to spin-dependent problems, such as Hamiltonians containing spin–orbit interactions. We show that for spin-independent Hamiltonians, a strict upper bound property is obeyed between a traditional Hartree–Fock-like determinant, full spinor wave function, the full determinant wave function, and a generalized spinor wave function. The relationship between a spinor wave function and the full determinant arises because the full determinant wave function is the spinor wave function projected onto a fixed-spin, after which antisymmetry is implicitly restored in the spin-independent case. For spin-dependent Hamiltonians, the full determinant wave function is not applicable, because it is not antisymmetric. Numerical experiments on the H₃ molecule and two-dimensional homogeneous electron gas confirm these bounds.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9612–9619"},"PeriodicalIF":5.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145074177","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":"Navigating the Unknown: Discovering Minimum Free Energy Pathways without Predefined End States","authors":"Zhicheng Zhong,&nbsp; and ,&nbsp;Qian Wang*,&nbsp;","doi":"10.1021/acs.jctc.5c00946","DOIUrl":"10.1021/acs.jctc.5c00946","url":null,"abstract":"<p >Determining minimum free energy pathways (MFEPs) for protein conformational changes is essential for molecular-level mechanistic understanding. While many robust path-search algorithms have existed, most require end point conformations derived from experimental structures, creating a dependency on structural biology data that restricts their general applicability. To overcome this limitation, we present a new path-search algorithm based on local sampling. The process can initiate from any single state, while the search direction is automatically optimized without the information on other states. We demonstrate the effectiveness of this algorithm in several model systems by comparing with experimental data and conventional molecular dynamics simulations. This approach expands the methodological toolkit for investigating functional conformational transitions, particularly when experimental end point structures are unavailable.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9943–9954"},"PeriodicalIF":5.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071790","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":"Machine Learning Navigated Allosteric Network to Unveil Biased Allosteric Modulation of GPCRs","authors":"Ming Kong,&nbsp;, ,&nbsp;Xin Chen,&nbsp;, ,&nbsp;Jun Mao,&nbsp;, ,&nbsp;Jin Yu,&nbsp;, ,&nbsp;Yuanpeng Song,&nbsp;, ,&nbsp;Yanzhi Guo,&nbsp;, and ,&nbsp;Xuemei Pu*,&nbsp;","doi":"10.1021/acs.jctc.5c00935","DOIUrl":"10.1021/acs.jctc.5c00935","url":null,"abstract":"<p >Biased allosteric modulators (BAMs) offer a promising avenue for developing safer and more selective therapeutics for G protein-coupled receptors (GPCRs). However, their molecular mechanisms remain unclear due to the complex combination of biased and allosteric characteristics. Motivated by the challenge, we proposed a machine learning navigated allosteric network strategy to address the issue. It consists of molecular dynamics simulation, a residue-level interpretable deep learning model, and allosteric network analysis, named as RMLNA. RMLNA first obtains biased conformation states through MD simulation and a density map. Then, an interpretable CNN-based classification model is utilized to identify important residues deciding the biased conformation. Navigated with these important residues, allosteric network analysis uncovers their regulation effects. With RMLNA, we revealed the biased allosteric modulation mechanism of a β-arrestin-biased modulator (SBI-553) for the clinically important target NTSR1. SBI-553 stabilizes a unique β-arrestin-biased state with an expanded intracellular binding site and the orthosteric ligand binding mode related to the β-arrestin-biased signaling. The interpretable deep learning model suggests that the middle and the lower parts of TM5 and TM6 are key determinants for the G protein/β-arrestin bias, while SBI-553 modulates the β-arrestin signaling mainly by H8 and the intracellular end of TM6 and TM7. Under the guidance of these results, the community network analysis underlines that the communication between TM5/6 and TM1/7 or TM2/4 is important for the β-arrestin-biased signaling, where SBI-553 redirects the communication between TM5/6 and TM1/7 via F8.50 of H8, inducing enhanced β-arrestin-biased signaling. NTS–NTSR1−β-arrestin complexes with and without binding of SBI-553 are constructed and simulated to further reveal the biased allosteric modulation mechanism to the β-arrestin and validate the reliability of the workflow. Collectively, this work provides molecular insights into the biased allosteric modulation of SBI-553 on NTSR1. More importantly, the novel computational workflow can be extended to other GPCRs.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9669–9686"},"PeriodicalIF":5.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071799","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":"Correlation-Driven Spin-Component-Scaled Second-Order Møller–Plesset Perturbation Theory (CD-SCS-MP2)","authors":"A. Paulau,&nbsp;, ,&nbsp;L. Soriano-Agueda*,&nbsp;, and ,&nbsp;E. Matito*,&nbsp;","doi":"10.1021/acs.jctc.5c01167","DOIUrl":"10.1021/acs.jctc.5c01167","url":null,"abstract":"<p >Møller–Plesset second-order perturbation theory (MP2) is one of the most popular and successful methods in computational chemistry, but it is not without disadvantages. It fails to capture nondynamic correlation, overestimates dispersion interactions in strongly polarizable systems, and inaccurately describes delocalized molecules. Spin-component scaling techniques improve MP2 energies by compensating for the fact that, in general, opposite-spin correlation plays a significantly greater role than same-spin correlation. On average, SCS-MP2 improves the reaction energies of small organic molecules, vibrational frequencies, thermodynamic properties, and π-stacking interactions; however, the optimal scaling values are known to be system-dependent, resulting in multiple SCS-MP2 methods. In this work, we propose improving the accuracy of SCS-MP2 by scaling the opposite-spin correlation according to the amount of dynamic correlation as measured from recently developed correlation indices that depend on the natural orbital occupations. In this way, the method is correlation-driven and can effectively adapt to the system-specific nature of spin-scaling factors. The correlation-driven SCS-MP2 (CD-SCS-MP2) method adds a negligible cost to the MP2 calculation and provides results superior to those obtained from SCS-MP2.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9601–9611"},"PeriodicalIF":5.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.5c01167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical Assessment of Curvature-Driven Surface Hopping Algorithms","authors":"Tomáš Jíra,&nbsp;, ,&nbsp;Jiří Janoš,&nbsp;, and ,&nbsp;Petr Slavíček*,&nbsp;","doi":"10.1021/acs.jctc.5c01176","DOIUrl":"10.1021/acs.jctc.5c01176","url":null,"abstract":"<p >Trajectory surface-hopping (TSH) methods have become the most used approach in nonadiabatic molecular dynamics. The increasingly popular curvature-driven schemes represent a subset of TSH based on the implicit local diabatization of potential energy surfaces. Their appeal partly stems from compatibility with machine-learning frameworks that often provide only local PES information. Here, we critically assess the limitations of these curvature-based algorithms by examining three challenging scenarios: (i) dynamics involving more than two strongly coupled electronic states; (ii) trivial crossings; and (iii) spurious transitions arising from small discontinuities in multireference potential energy surfaces. Furthermore, we extend the Landau–Zener surface hopping (LZSH) method beyond two-state systems and introduce practical modifications to enhance its robustness. The performance is benchmarked on both low- and higher-dimensional model Hamiltonians, as well as realistic molecular systems treated with <i>ab initio</i> methods. While curvature-driven TSH using the explicit electronic coefficient propagation qualitatively captures the dynamics in most cases, we find no regime where it outperforms LZSH, especially when trivial crossings, multistate crossings, or discontinuities are encountered. Hence, we advocate for using a conceptually simple but solid LZSH method when nonadiabatic couplings are not available.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9784–9798"},"PeriodicalIF":5.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.5c01176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering Dickerson–Drew DNA Equilibrium beyond the BI/BII DNA Dichotomy by Interpretation of 31P NMR Parameters","authors":"Jiří Fukal,&nbsp;, ,&nbsp;Miloš Buděšínský,&nbsp;, ,&nbsp;Jakub Šebera,&nbsp;, ,&nbsp;Marie Zgarbová,&nbsp;, ,&nbsp;Petr Jurečka,&nbsp;, and ,&nbsp;Vladimír Sychrovský*,&nbsp;","doi":"10.1021/acs.jctc.5c01076","DOIUrl":"10.1021/acs.jctc.5c01076","url":null,"abstract":"<p >DNA duplexes exist as dynamic ensembles of interconverting conformations in solution. Conventional nuclear magnetic resonance (NMR) data interpretation often simplifies this behavior by assuming one dominant structure, but multiple substates (such as different backbone conformers) can coexist. Here, we present an approach that refines the interpretation of ³¹P NMR data in the Dickerson–Drew DNA by integrating a nucleotide conformational classification (NtC) (Černý et al., <i>Nucleic Acids Research</i> 2020, <b>48</b>, 6367–6381) with molecular dynamics (MD) simulations. By finely classifying backbone conformers into distinct NtC-defined states and using MD to predict their populations, we achieve a more nuanced correspondence between experimental NMR observables and DNA structure-dynamical heterogeneity. Application of this framework demonstrates a radical improvement of NMR data interpretation, thereby enhancing the reliability of deducing DNA conformational equilibria in solution.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"10006–10017"},"PeriodicalIF":5.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.5c01076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSCF+: An Extended and Improved Open-Source Software Package for Polymer Self-Consistent Field Calculations","authors":"Juntong He,&nbsp; and ,&nbsp;Qiang Wang*,&nbsp;","doi":"10.1021/acs.jctc.5c01171","DOIUrl":"10.1021/acs.jctc.5c01171","url":null,"abstract":"<p >We introduce PSCF+, an extended and improved open-source software package for polymer self-consistent field (SCF) calculations of block copolymer self-assembly. PSCF+ supports various chain models (including the continuous Gaussian chains, discrete Gaussian chains, and freely jointed chains), nonbonded isotropic pair repulsions (including the Dirac δ-function, Gaussian, soft-sphere, and dissipative particle dynamics potentials), and system compressibility (compressible vs incompressible), thus enabling direct comparisons with molecular simulations and field-theoretic simulations based on the same model system without any parameter-fitting. Several recently proposed algorithms are implemented in PSCF+ to greatly reduce the GPU memory usage and speed-up the SCF calculations, including the Richardson-extrapolated pseudospectral methods for solving the modified diffusion equations, the crystallographic discrete cosine transforms taking advantage of the partial symmetry of some ordered phases, and the “slice” algorithm for storing chain propagators. It also avoids redundant calculations and storage of propagators for chain architectures such as bottlebrush block copolymers, and uses an improved iterative scheme for solving SCF equations in athermal solvent conditions. Last but not least, it implements the automated calculation along a path to efficiently calculate free-energy curves and phase boundaries. PSCF+ is freely available and remains under active development, with further extensions planned to broaden its applicability to more complex polymeric systems.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9879–9889"},"PeriodicalIF":5.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068345","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":"Understanding Electronic Excitations Between Single Determinants with Occupied-Virtual Orbitals for Chemical Valence","authors":"Hengyuan Shen,&nbsp;, ,&nbsp;Nicola Bogo,&nbsp;, ,&nbsp;Christopher J. Stein,&nbsp;, and ,&nbsp;Martin Head-Gordon*,&nbsp;","doi":"10.1021/acs.jctc.5c01029","DOIUrl":"10.1021/acs.jctc.5c01029","url":null,"abstract":"<p >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 <i>e</i>^– 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.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 19","pages":"9525–9537"},"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}