Journal of Chemical Theory and Computation最新文献

{"title":"Unveiling Electric Field-Driven Stereocontrol in Hurd–Claisen Rearrangements","authors":"Mateus Rodrigues Barbosa,&nbsp;Pedro H. F. Matias,&nbsp;Daniel F. Scalabrini Machado,&nbsp;Angelo H. Lira Machado,&nbsp;Heibbe C. B. de Oliveira","doi":"10.1021/acs.jctc.6c00732","DOIUrl":"10.1021/acs.jctc.6c00732","url":null,"abstract":"<p>Herein, we present a comprehensive computational studyof the Hurd–Claisenrearrangement in both ester- and nitrile-containing substrates underoriented and nonoriented external electric fields (EEFs). Using high-levelDFT calculations, we systematically mapped how EEFs perturb dipole–fieldinteractions and transition-state geometries across a wide range ofaryl and alkyl substituents. Fields aligned along the traditionalreaction axis impart only modest differential stabilization (0.5–1.0kcal·mol^–1), insufficient to reverse <em>E</em>/<em>Z</em> ratios except in nearly degeneratecases, whereas molecular-axis alignment boosts this difference toover 2 kcal·mol^–1, reliably inverting selectivityby maximizing dipole overlap. To bridge theory and experiment, wehave applied the gold–thiolate anchoring in our model, harnessingsubstrate polarization to amplify transition-state stabilization fromroughly 5 kcal·mol^–1 in the free system toover 20 kcal·mol^–1 at higher field strengths.We further show, via isotropic field ensembles weighted by Boltzmannand von Mises–Fisher statistics, that even nonoriented fieldscan impose a stereochemical bias of at most 2.5 kcal·mol^–1 through dipole torque. These findings establish dipoleorientation as a quantitative predictor of EEF-mediated stereocontroland provide concrete blueprints for deploying “invisible catalysts”in flow reactors, surface-confined assemblies, and microreactor platformsfor reagent-free, on-demand synthesis of stereochemically definedmolecules.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11419–11431"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148313877","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":"LipidMem-BigSMILES (LiMBS): A Standardized Representation Language for Lipid Membrane Systems","authors":"Pratima Khadka,&nbsp;Shikha Nangia","doi":"10.1021/acs.jctc.6c01274","DOIUrl":"10.1021/acs.jctc.6c01274","url":null,"abstract":"<p>Molecular dynamicssimulations of lipid membranes lack a standardized,machine-readable representation, creating significant barriers toreproducibility, data sharing, and AI-driven analyses. Current membranedescriptions are fragmented across software-specific input files,making it difficult to reconstruct published systems or compare resultsamong studies. Here, we introduce LipidMem-BigSMILES (LiMBS), a linenotation not a simulation tool or membrane-building program, for lipidmembrane simulations that extend SMILES, BigSMILES, and CG-BigSMILESto membrane systems. LiMBS version 1.0 uses a dual-block architecturethat separates a system-level descriptor block from a chemical structureblock, enabling the independent specification of membrane geometry,composition, environmental conditions, and molecular identity. Theframework supports planar bilayers, vesicles, and bacterial outermembranes in atomistic, coarse-grained, and supercoarse-grained resolutionswithin a unified syntax. We provide a reference tool that reads, checks,and standardizes LiMBS strings, and we show that its output can behanded off to existing membrane-building software for system construction.LiMBS1.0 is human-readable, machine-readable, tool-independent, andcompatible with FAIR data principles, providing a standardized descriptionlayer for membrane simulations that supports reproducible reporting,interoperable databases, and AI-enabled membrane biophysics.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11634–11646"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149275048","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":"Data-Driven Approach for Predicting Long-Time Anomalous Transport from Short Trajectories","authors":"Chuyi Liu,&nbsp;Yifeng Guan,&nbsp;Jingyuan Li,&nbsp;Mao Su","doi":"10.1021/acs.jctc.6c01846","DOIUrl":"10.1021/acs.jctc.6c01846","url":null,"abstract":"<p>Molecularsimulations provide detailed microscopic trajectoriesbut often do not reach the long-time transport regime. Predictinglong-time transport from ensembles of short trajectories is particularlychallenging in heterogeneous materials, where static spatial disorderand viscoelastic memory can produce similar transient dynamics whileleading to different behaviors at longer times. We introduce the spatial-disordergeneralized Langevin equation (SD-GLE), a likelihood-based generativeframework that represents local effective potential landscapes asrealizations of a Gaussian random field and temporal correlationsthrough a memory kernel; variational Bayesian inference jointly learnsboth components from short trajectory ensembles and uses them to generatelong trajectories in statistically equivalent environments. In controlledbenchmarks, SD-GLE recovers the prescribed disorder covariance andmemory kernel, while application to randomly cross-linked polymernetworks demonstrates an accurate prediction of size-dependent long-timeand non-Gaussian tracer transport, including the crossover to hopping-dominatedmotion. By linking short-time microscopic motion that departs fromsimple diffusion to long-time transport behavior, SD-GLE providesa bottom-up modeling strategy that is applicable to a broad rangeof molecular systems with complex transport dynamics.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11183–11192"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149281898","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":"Martini 3 Overestimates Lipid Scrambling by Membrane Proteins","authors":"Yiyang Wang,&nbsp;Zhouyu Zhang,&nbsp;Jeremy E. Arndt,&nbsp;Madeleine Grisard,&nbsp;Chen Song,&nbsp;Yiechang Lin,&nbsp;Ben Corry","doi":"10.1021/acs.jctc.6c00949","DOIUrl":"10.1021/acs.jctc.6c00949","url":null,"abstract":"<p>Coarse-grainedmolecular dynamics simulations are widelyused tostudy membrane proteins that mediate lipid translocation between bilayerleaflets, yet the reliability of current force fields for identifyingand characterizing such lipid scrambling has not been systematicallyassessed. Here we find that around 20% of membrane proteins scramblelipids in Martini 3 simulations in the MemProtMD database, representinga 15-fold increase compared to the older Martini 2.2 force field.Martini 3 simulations overestimate lipid-scrambling rates byan average of 66-fold compared with Martini 2.2 and exceedexperimentally measured rates. They also predict scrambling throughclosed scramblases and membrane proteins known not to scramble lipids,highlighting their limitations for studying lipid scrambling and lipid–proteininteractions. Small refinements in the Martini 3 force-field parameterscan remove spurious lipid scrambling, suggesting avenues for improvingthe accuracy of this model.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11608–11616"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148858540","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 Interaction for Anharmonic Vibrational Spectroscopy and Quantum-Sampled Configuration Spaces","authors":"Raphael F. Ligorio,&nbsp;Marco Antonio Barroca,&nbsp;Alan Duriez,&nbsp;Mathias B. Steiner","doi":"10.1021/acs.jctc.6c01564","DOIUrl":"10.1021/acs.jctc.6c01564","url":null,"abstract":"<p>Quantum-centric workflows are a promising route to improvingtheaccuracy of property predictions in computational chemistry and materialsscience. By integrating quantum sampling algorithms with classicalsolvers, electronic structure calculations have recently demonstratedtheir potential even on noisy intermediate-scale quantum devices.In principle, the method of Vibrational Configuration Interaction(VCI) is suitable for integration with quantum sampling algorithmsas well. However, demonstrations of computational workflows for quantum-centric,vibrational property predictions are still lacking. Here, we introducea methodology for performing anharmonic vibrational structure calculationsthat can be deployed in a hybrid, quantum-classical mode. Startingfrom a quartic force field, the approach combines a Vibrational Self-ConsistentField (VSCF) with VCI in Full, Selected (S-VCI), or Symmetry-Adapted(SA-VCI) modes. In S-VCI, an Epstein–Nesbet perturbative screeningsignificantly reduces the configuration space while retaining highpredictive accuracy. A state-list input enables the integration ofexternally generated vibrational configurations as a seed space. Asa proof-of-concept, we demonstrate a hybrid, quantum-classical computationalworkflow in which a quantum sampling algorithm provides the seed.Our vibrational wave function analysis package <span>ViBra</span>, equippedwith a graphical interface, is available at https://github.com/raphafe96/ViBra.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11498–11518"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","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":"Reconstructing All-Electron Densities and Static Structure Factors from General Pseudopotentials","authors":"Sebastian R. Thomsen,&nbsp;Anker M. H. Nielsen,&nbsp;Bo B. Iversen,&nbsp;Frank Jensen","doi":"10.1021/acs.jctc.6c01398","DOIUrl":"10.1021/acs.jctc.6c01398","url":null,"abstract":"<p>Electron densities provide valuableinformation, suchas atomiccharges and static structure factors, but all-electron densities canbe computationally expensive to obtain. This work presents a methodto approximate all-electron densities from pseudopotential electronicstructure calculations, called the Valence Electrons Plus IndependentCores (VEPIC) model. For each pseudopotential in the calculation,a pseudocore is defined, and this is added to the <em>ab initio</em> pseudovalence density. Electron densities obtained this way yieldaccurate static structure factors, as well as atomic charges and dipolesfrom the Hirshfeld and Minimal Basis Iterative Stockholder methods,compared with those from all-electron calculations. While the totaldensity of this model matches the all-electron density in the valenceregion, the model lacks core polarization, which is present in theProjector Augmented Wave (PAW) method, making the VEPIC model an intermediatebetween the Independent Atom Model (IAM) and the PAW model in termsof accuracy. It is also intermediate between the two in terms of computationalcomplexity, as it only requires the pseudovalence density, unlikePAW, which additionally requires processing every pseudovalence wavefunction in the system.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11372–11377"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221627","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":"MARS: Machine-Learned Force Field Framework for Automated Conformational Sampling, Vibrational Spectroscopy, and Microsolvation","authors":"Sergio Suárez-Dou,&nbsp;Miguel Gallegos,&nbsp;Florian N. Brünig,&nbsp;Alexandre Tkatchenko","doi":"10.1021/acs.jctc.6c01588","DOIUrl":"10.1021/acs.jctc.6c01588","url":null,"abstract":"<p>We present MARS, an open-source Python package for automatedconformationalensemble generation, infrared spectrum prediction, and explicit microsolvationof molecular systems. Built on JAX for GPU acceleration and automaticdifferentiation, MARS is carried out with machine-learned force fields(MLFFs), including SO3LR and MACE, with a four-phase conformationalsampling workflow combining metadynamics, multitemperature moleculardynamics, and genetic algorithms. The package provides automated vibrationalmode classification using Wilson B-matrix projections and functionalgroup identification via graph-based pattern matching. We benchmarkMARS on chemically diverse systems, including alkanes, halogenatedaromatics, amino acids, peptides, drug-like molecules, phosphamides,host–guest complexes, and microsolvated biomolecular models.Semiempirical quantum-chemical methods have until now provided thepractical state of the art for automated conformational sampling.Our benchmarks show that modern MLFFs can improve upon this paradigmby combining greater accuracy for high-level relative conformer energieswith lower computational cost and more favorable scaling with molecularsize. IR benchmarks against experimental gas-phase spectra additionallyshow performance comparable to quantum-chemical methods, with chemicallyinterpretable mode assignments. Applications to alanine peptides,proline/β-cyclodextrin inclusion complexes, alanine dipeptide,and 2-methoxycyclohexanone further demonstrate that MARS capturesconformational reorganization, noncovalent binding motifs, solvent-inducedstability shifts, and spectroscopic signatures. These results establishMARS as a transferable and accessible platform for ensemble-basedmolecular modeling with machine-learned potentials. MARS is freelyavailable at github.com/tcpunilu/mars.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11125–11141"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149226482","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":"Paired-Isotopologue Hessian Benchmarking for Second-Order Validation of Machine-Learned Water Potentials","authors":"Yihao Wen,&nbsp;Xue Wen,&nbsp;Baoxin Ni,&nbsp;Wenxin Zhao,&nbsp;Sida Li,&nbsp;Wanglin Qu,&nbsp;Zi Wang,&nbsp;Yuqiang Li,&nbsp;Hao Wei","doi":"10.1021/acs.jctc.6c01692","DOIUrl":"10.1021/acs.jctc.6c01692","url":null,"abstract":"<p>Machine-learned interatomic potentials (MLIPs) are commonlyvalidatedagainst energies and forces, yet vibrational predictions depend onthe curvature and mode information encoded in the Hessian of the potential-energysurface. Here, we introduce a paired-isotopologue Hessian benchmarkfor MLIPs using hydrogen-bonded water as a test system. For each MD-sampledwater-cluster geometry, a common electronic Hessian is combined withmatched HH, DH, and DD mass patterns, enabling the controlled evaluationof vibrational frequencies, normal modes, and isotope responses acrossnine universal, molecular, and water-specific potentials. Model rankingsdiffer across force-, Hessian-, and vibration-based evaluations, whilethe paired isotopologue response follows the broad vibrational orderingand remains consistent across structural sources. Beyond global errormeasures, local coordinate analysis distinguishes Hessian errors withmarkedly different relevance to specific vibrational observables.Along isotope paths, near-degenerate modes can lose individual modeoverlap without a corresponding loss of the vibrational subspace.Together, these results establish Hessian and mode-space validationas necessary complements to energy- and force-based evaluations ofMLIPs intended for vibrational calculations.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11519–11529"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149281858","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":"A Machine-Learned Symbolic Committor for a Chemical Reaction: Isomerization in Retinal and p-Coumaric Acid","authors":"Kai Töpfer,&nbsp;Gianmarco Lazzeri,&nbsp;Vittoria Ossanna,&nbsp;Christopher J. I. Bläß,&nbsp;Florian Renner,&nbsp;Gianluca Lattanzi,&nbsp;Roberto Covino,&nbsp;Bettina G. Keller","doi":"10.1021/acs.jctc.6c01496","DOIUrl":"10.1021/acs.jctc.6c01496","url":null,"abstract":"<p>The thermal <em>cis</em>-<em>trans</em> isomerizationof C═C double bonds in chromophores is a prototypical high-barrierreaction whose mechanism hinges on subtle out-of-plane bending motions.Combining machine-learning-guided path sampling (AIMMD) with symbolicregression, we learn an interpretable committor for two chemicallydistinct chromophores, <em>N</em>-retinylidene-lysine and <em>p</em>-coumaric acid anion, in vacuo from unbiased moleculardynamics trajectories generated by two-way shooting. Parametrizingthe committor with a neural network and sampling it uniformly acrossthe full range allows the model to resolve the reaction coordinateacross the full transition region, not only at the isocommittor surface <em>p_B</em>(<strong>x</strong>) = 0.5. A feature-attributionanalysis identifies four proper dihedrals around the reactive bondas the informative coordinates, while the improper dihedrals at thesp²-hybridized carbon atoms prove unsuitable because reactant,transition, and product states share the same values. Symbolic regressionthen distills the committor into compact analytical expressions─toour knowledge the first committor both learned and distilled intoclosed form for a chemical reaction─and shows that a couplingof all four dihedrals is required to reproduce the S-shaped, stepwisepathway seen in the transition-path ensemble. This S-shape is absentfrom the minimum free-energy path: it arises from the dynamics ofthe short transition events combined with the mass asymmetry betweenheavy-atom and hydrogen-bearing dihedrals, and the larger asymmetryof <em>p</em>-coumaric acid yields the more pronounced S-shapethis picture predicts. An interpretable, machine-learned committor,though a function of configuration alone, thus exposes dynamical featuresof the mechanism to which the free-energy surface is blind. The workflowrequires no prior assumptions about the reaction coordinate and extendsnaturally to other high-barrier isomerizations and, combined witha reactive potential, opens a path toward bond-breaking chemical reactions.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"22 21","pages":"11233–11250"},"PeriodicalIF":5.8,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149304120","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":"Can Phaseless Auxiliary-Field Quantum Monte Carlo with Broken Symmetry Trials Describe Iron–Sulfur Clusters?","authors":"Eirik F. Kjønstad,Huanchen Zhai,James Shee,Sandeep Sharma,Garnet Kin-Lic Chan","doi":"10.1021/acs.jctc.6c01749","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01749","url":null,"abstract":"Phaseless auxiliary-field quantum Monte Carlo (AFQMC) has in several cases been found to perform well on strongly correlated systems, including compounds containing transition-metal elements. Here, we benchmark the method for three iron–sulfur clusters ([2Fe-2S], [4Fe-4S], and the FeMo cofactor) using a hierarchy of systematically improved trial states derived from coupled cluster (CC) theory, including up to quadruple excitations relative to the Hartree–Fock (HF) reference, as well as multi-Slater trial states derived from unrestricted density matrix renormalization group wave functions. Our results reveal for these systems that, as the symmetry-broken trial is improved beyond the mean-field level, the phaseless AFQMC energy can become less accurate, and in some cases even less accurate than the underlying trial projected energy itself, displaying an inverted energy pattern that is only corrected once the trial fidelity is sufficiently high. For [2Fe-2S], we show that a decreased accuracy in the AFQMC energy can coincide with a simultaneous improvement in the trial state and the walker ensemble, as measured by their fidelity relative to the exact ground state, a behavior which is allowed by the nonvariational nature of the phaseless AFQMC energy estimator. We further find that this is not solely due to the use of spin-unrestricted trial states, as the inversion persists in [2Fe-2S] when we explicitly break the symmetry of the Hamiltonian by applying a fictitious local spin-Zeeman field. Instead, we find that the energy inversion is related to the choice of measurement trial in the estimator, where using a high-order CC trial state for measurements may introduce errors that are suppressed when the measurement wave function is restricted to lower excitation subspaces. In particular, measuring the energy with the mean-field reference while guiding the walkers with a CC trial removes the inverted pattern and improves the overall accuracy across the iron–sulfur clusters, with a possible exception for [4Fe-4S] where energies with a CCSDT guide may be less accurate than those guided by CCSD for some broken-symmetry families. Taken together, our findings suggest that the relatively accurate energies obtained with an HF trial state in these systems arise from favorable error cancellation, warranting significant caution about the reliability of phaseless AFQMC with such trials for strongly correlated transition-metal systems of this kind.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"64 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149368312","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}