Journal of Chemical Theory and Computation最新文献

{"title":"Comparing Symmetry-Adapted\u0000Perturbation Theory to\u0000Intermolecular Energy Decomposition Analysis Based on Absolutely Localized\u0000Molecular Orbitals","authors":"Narayanan Puthillathu,Montgomery Gray,John M. Herbert","doi":"10.1021/acs.jctc.6c01446","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01446","url":null,"abstract":"Abstract Energy decomposition analysis (EDA) can provide insight into noncovalent interactions, even though the individual energy components (electrostatics, Pauli repulsion, polarization, dispersion, etc.) are not uniquely defined within quantum mechanics. The present work examines the two titular EDA schemes and elucidates the degree of transferability between their components. We first demonstrate that a many-body dispersion (MBD) model can replace the conventional definition of dispersion in symmetry-adapted perturbation theory (SAPT), preserving reasonable accuracy as compared to third-order SAPT but reducing the computational cost to be comparable to density functional theory (DFT). Individual SAPT + MBD energy components exhibit good correlation with those computed using the absolutely localized molecular orbital (ALMO) EDA scheme, which is built around DFT, such that qualitative explanations and chemical trends are likely to be similar in either approach. SAPT + MBD and ALMO-EDA electrostatic energies are nearly identical, provided that a SAPT-style direct-product reference state is used for ALMO-EDA, but other energy components exhibit substantial quantitative differences. These conclusions hold across a range of density functionals used in the ALMO-EDA calculations, and for different choices of the “dispersion-free” functional that is used to isolate the dispersion energy from the target functional’s interaction energy. The present work provides calibration plots to translate between SAPT + MBD and ALMO-EDA energy components. For a large data set of dimers representing ligand–protein interactions, SAPT + MBD calculations afford superior agreement with the best ab initio benchmarks, as compared to calculations using the state-of-the-art ωB97M-V functional, especially when triple-ζ basis sets are used for SAPT. This conclusion holds even when three-body (Axilrod–Teller–Muto) dispersion corrections are added to the ωB97M-V results.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"20 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151226","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":"Distilling First-Principles Accuracy into Compact\u0000Machine Learning Potentials for Condensed-Phase Chemistry","authors":"Sijia Chen,Niamh O’Neill,Benjamin X. Shi,Venkat Kapil","doi":"10.1021/acs.jctc.6c01131","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01131","url":null,"abstract":"Abstract Accurate machine learning interatomic potentials (MLIPs) have made first-principles-quality potential energy surfaces increasingly accessible for condensed-phase chemistry, but their inference cost can still limit the sampling needed to compute experimentally relevant observables. In this work, we use knowledge distillation to construct compact “student” models that retain the accuracy of much larger “teacher” models obtained through either finetuning of foundation models or Δ-learning. The resulting students reduce production simulation cost by approximately an order of magnitude, making high-accuracy sampling practical for challenging condensed-phase problems. We demonstrate this across three problems of increasing sampling complexity: finite-temperature NPT simulations of ice Ih, classical and path-integral simulations of liquid water over 240–370 K, and path-integral umbrella-sampling simulations of water dissociation at the anatase TiO2(101)/water interface. When compared, the distilled students reproduce the target observables of their teachers more reliably than equally compact models trained only on the available reference data. The liquid water student, distilled from a Δ-learned CCSD(T)-quality teacher, reproduces thermodynamic, structural, transport, and nuclear quantum effects over the full temperature range studied. At the TiO2/water interface, knowledge distillation makes PIMD umbrella sampling practical and shows that nuclear quantum effects lower the dissociation barrier by approximately 2 kcal/mol and shift the molecular–dissociated free energy difference into closer agreement with recent solid-state 17O NMR measurements. Our work demonstrates how knowledge distillation can make accurate MLIPs practical for the sampling methods needed to connect condensed-phase reaction thermodynamics with experiment, notably for interfacial chemistry and catalysis.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151227","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":"Comparative Analysis\u0000of Relative Ligand Binding Free\u0000Energy Simulation Methods: Amber-TI, GROMACS-NETI, OpenMM-FEP, and\u0000BLaDE-MSLD","authors":"Hajin Lee,Injung Kim,Sungjun Kim,Minsik Bae,Bomi Jeong,Seonghoon Kim,Sunhwan Jo,Jumin Lee,Wonpil Im","doi":"10.1021/acs.jctc.6c01744","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01744","url":null,"abstract":"Abstract Structure-based drug design has become increasingly important in the pharmaceutical industry for accelerating the discovery of effective drug candidates. In particular, ligand binding free energy serves as a critical metric for predicting drug efficacy during the key stages of hit discovery and lead optimization. Continuous progress has been made in the prediction of ligand binding free energies, but direct comparisons of different methods using the same force field remain challenging due to their unique implementations in different simulation engines. In this study, we present a direct comparison of four popular methodologies (Amber-TI, GROMACS-NETI, OpenMM-FEP, and BLaDE-MSLD) for calculating relative binding free energies (ΔΔGbind) with the same Amber protein and ligand force fields using MolCube Alchemical Free Energy Simulator (MolCube-AFES), which provides an input generation workflow to support ΔΔGbind calculations of all four methods. We used 80 alchemical transformations (among the JACS benchmark set by Wang et al.) and two additional applications to compare the predicted ΔΔGbind from the four methods against experimental measurements. All four methods reproduced experimentally observed trends, with most transformations within ±2 kcal/mol from experiments, and showed broadly comparable accuracy with no statistically significant performance differences across the benchmark data set. These results demonstrate that MolCube-AFES enables controlled, cross-platform benchmarking and show that all four different alchemical free energy methods deliver statistically equivalent accuracy, with method selection guided by workflow requirements such as throughput, portability, and perturbation network design rather than expected differences in performance.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151269","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":"Hamiltonian-Aware ADAPT Variational Quantum Eigensolver\u0000for Molecular Ground-State Simulation","authors":"Runhong He,Xin Hong,Qiaozhen Chai,Chao Liu,Junyuan Zhou,Ji Guan,Guolong Cui,Shenggang Ying","doi":"10.1021/acs.jctc.6c01185","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01185","url":null,"abstract":"Abstract Designing compact ansätze for the Variational Quantum Eigensolver (VQE) is crucial for calculating energies of large molecules on near-term quantum devices. However, the widely used Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT) ansätze present two challenges: the inherent locality of conventional criteria results in inappropriate excitation operator selection, and the inevitable degradation of certain operators gives rise to redundant accumulation. In this paper, we propose the Hamiltonian-Aware (HA) ADAPT-VQE algorithm to address these issues. First, we present a novel excitation operator selection criterion, which overcomes the locality constraint of existing criteria by incorporating Hamiltonian information. It effectively avoids selecting ineffective operators by prioritizing physically meaningful ones, and incurs no extra classical or quantum computational overhead. Second, we develop a new problem-adaptive method for discriminating and pruning redundant excitation operators stemming from improper selection and inevitable degradation. This method balances redundant operator pruning and convergence guarantee, and is applicable to ansätze with arbitrary scales. Systematic numerical experiments on typical strongly correlated molecular systems demonstrate that our HA-ADAPT-VQE mitigates energy plateaus and outperforms baseline algorithms in terms of energy error, ansatz size, and measurement cost in most cases. This work offers an efficient, robust ansatz construction paradigm, facilitating the development and practical deployment of large-scale VQE in quantum chemistry.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"32 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151268","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 Inference\u0000of Susceptibility, Correlation,\u0000and Dynamic Responses via Machine Learning","authors":"Kaihua Zhang,Fanlong Meng,Shuanhu Qi,Ying Jiang","doi":"10.1021/acs.jctc.6c01183","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01183","url":null,"abstract":"Abstract Understanding how complex systems respond to external perturbations is essential to characterize the intrinsic properties of the systems and then to control their responses. Despite some preliminary trials via either analytical modeling or numerical calculations, it is usually difficult to directly calculate the responses of complex systems (with high degrees of freedom) to strong perturbations. Here, we propose a flexible and data-driven protocol based on neural networks, allowing us to infer the responses of complex systems from collected data, while seamlessly incorporating prior physical knowledge to ensure consistency with underlying laws. We validate this protocol using four representative examples, by calculating susceptibility in ϕ4 theory, N-point correlation function of homopolymers, dynamic response function of homopolymer blends, and Jacobian matrix for determining the stability of the Duffing system, from the data generated by numerical calculations or simulations. In all cases, the inferred responses are consistent with theoretical results, without imposing analytical approximations used to derive the reference solutions. These examples demonstrate the applicability of the proposed protocol for extracting response functions in diverse physical systems and provide a basis for its extension to related problems in other research fields.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151270","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":"Determining the Partial Molar Volume of the Hydrated\u0000Electron from First-Principles Simulated Solution Volumes","authors":"Mengxu Li,Ruoting Zhao,Chenyu Zhang,Tianhang Zhou,Yan Su,Jijun Zhao,Jinggang Lan","doi":"10.1021/acs.jctc.6c01454","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01454","url":null,"abstract":"Abstract The microscopic solvation structure of the hydrated electron remains difficult to characterize directly despite extensive experimental and theoretical efforts. The partial molar volume,V¯e−, provides an indirect thermodynamic probe, but quantitative agreement with experiment is challenging due to the strong dependence on the theoretical model. Here, we present a first-principles approach, accelerated by machine learning potentials, for determining V¯e− from volume differences among charge-neutral H2O, HCl, NaCl, and Na–eaq– reference systems. Rather than relying on direct NPT sampling, we determine the equilibrium volumes from pressure–density relations obtained through NVT simulations. Their system size dependence is described using a physically motivated density–volume relation, from which V¯e− is extracted by extrapolation to infinite dilution. The resulting V¯e− is 26 ± 7 cm3 mol–1 on the absolute single ion scale, statistically consistent with the experimental estimate of 26 ± 6 cm3 mol–1. This agreement demonstrates that the present framework can connect microscopic solvation structure with experimentally accessible thermodynamic properties and offers a transferable approach for other aqueous systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"32 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151272","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":"RACE: A Modular\u0000and Interpretable Radial–Angular\u0000Correlation Descriptor for Atomistic Machine Learning","authors":"Raphael M. Tromer,Jhionathan de Lima,Cristiano Francisco Woellner,Paola Ferreira Barbosa,Roberto Ventura Santos,Luiz Antonio Ribeiro Junior","doi":"10.1021/acs.jctc.6c01111","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01111","url":null,"abstract":"Abstract Atomistic machine learning (ML) representations involve trade-offs among predictive accuracy, physical interpretability, descriptor dimensionality, and computational cost. Here, we introduce the Radial–Angular Correlation Descriptor (RACE), a modular representation constructed from radial distributions, angular distributions, and an explicit joint radial–angular map. Chemical identity is incorporated through interchangeable scalar or multichannel weighting functions rather than through a mandatory set of element-pair channels. We evaluate RACE against conventional smooth overlap of atomic positions (SOAP), compressed SOAP-μ2, atom-centered symmetry functions (ACSF), and a fixed-basis atomic cluster expansion (ACE) representation using common train–test partitions, training-only feature selection, repeated evaluations, and uncertainty estimates. RACE provides useful predictive accuracy across structurally and chemically diverse systems, but it does not uniformly outperform the alternative representations. Compressed SOAP is particularly accurate for the chemically heterogeneous benchmarks, whereas explicit composition features are required for robust generalization across broad Materials Project chemical spaces. Direct ablation of the radial, angular, and joint channels further shows that the incremental value of the joint radial–angular term is system dependent. The principal practical characteristics of RACE are therefore its transparent component structure, configurable chemical encoding, descriptor dimension independent of the number of species in its scalar-weighted form, and low peak memory during descriptor generation. RACE is consequently presented as a simple and manipulable structural representation that complements, rather than universally replaces, systematic density-correlation descriptors.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"20 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151275","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":"Development and\u0000Bayesian Uncertainty Quantification\u0000of Transferable Coarse-Grained Models for Carbohydrates","authors":"Parisa Farzeen,Soumil Y. Joshi,Abhishek T. Sose,Sanket A. Deshmukh","doi":"10.1021/acs.jctc.6c01202","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01202","url":null,"abstract":"Abstract Carbohydrates play an important role in industrial, biomedical, and materials applications. The exceptional structural diversity of carbohydrates makes their accurate molecular modeling both essential and challenging. Although coarse-grained (CG) molecular dynamics (MD) simulations provide access to large length and time scales relevant to carbohydrate assembly and materials behavior, the development of uncertainty-quantified transferable CG models remains challenging and limited. In this work, we employ a computational framework that integrates multiscale modeling, optimization algorithms, and machine learning to develop CG models that reproduce the structural, physical, and thermodynamic properties of 12 pyranose monosaccharides. To preserve their stereochemical and geometric features while balancing computational efficiency, each monosaccharide is represented using a six-bead mapping scheme, with each CG bead mapped based on the monosaccharide functional groups. To ensure transferability, force field (FF) parameters for CG models of three model monosaccharides, α-d-glucose, β-d-xylose, and α-l-rhamnose, were optimized using Particle Swarm Optimization (PSO). Bayesian uncertainty quantification was subsequently performed to assess the robustness of the optimized models by quantifying uncertainty in both the model parameters and the predicted properties. To assess parameter transferability, we applied the optimized CG beads developed for the model monosaccharides to other monosaccharides in CG MD simulations. The resulting CG models accurately reproduced thermodynamic and structural properties across 12 monosaccharides, demonstrating good transferability. Finally, the predictive capability of the optimized CG parameters was further assessed across 12 monosaccharides using glass transition temperatures and surface energies, which were not included during parameter optimization. The model reproduced glass transition temperature with good accuracy and captured qualitative surface-energy trends, demonstrating predictive capability beyond the optimized properties. Overall, this work establishes an uncertainty-aware CG modeling framework that enhances confidence in CG simulations and provides a scalable foundation for extending to oligosaccharides, polysaccharides, and hydrated carbohydrate assemblies.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151328","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":"Local Correlation-Based\u0000Machine Learning Enables Coupled-Cluster-Level\u0000Energetics for Thousand-Atom Molecular Systems","authors":"Hua Feng,Shuhua Li,Jing Ma,Wei Li","doi":"10.1021/acs.jctc.6c01569","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01569","url":null,"abstract":"Abstract Data-intensive molecular workflows increasingly require consistent energies for many molecules and configurations. High-accuracy data are much scarcer because the small energy differences governing conformational preferences and molecular recognition can require an accurate treatment of electron correlation. Coupled-cluster singles and doubles with perturbative triples [CCSD(T)] provides a widely used benchmark, but repeated calculations become prohibitive for medium-to-large molecular systems. Machine learning has enabled rapid coupled-cluster-quality predictions for small molecules, particularly by learning high-level corrections to lower-cost calculations, yet its extension to large systems remains limited by expensive full-system labels and increasingly complex electronic representations. Here we develop cluster-in-molecule machine learning (CIM-ML), which learns orbital-pair corrections from CIM resolution-of-the-identity second-order Møller–Plesset perturbation theory (CIM-RI-MP2) to CIM domain-based local pair natural orbital CCSD(T) [CIM-DLPNO–CCSD(T)]. Fixed CIM locality criteria define bounded learning environments, and the CIM energy expression assembles local predictions into an extensive total energy. A model trained on one (H2O)128 conformer transfers to additional conformers and (H2O)237, reproducing conformational-energy separations within approximately 1 kcal mol–1. For a 1,027-atom protein–ligand complex, training on 500 local clusters reduces the interaction-energy mean absolute error from 12.92 to 3.98 kcal mol–1. CIM-ML thus provides a domain-adaptable route for reusing high-level correlation information across large molecular ensembles.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"82 1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151274","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":"An Improved DFTB/Force\u0000Field Model for Molecule–Rare\u0000Gas Interactions: Application to 1,4-Diazabicyclo[2.2.2]octane (DABCO)\u0000Solvated by Argon","authors":"Paul Guibourg,Fernand Spiegelman,Audrey Scognamiglio,Lionel Poisson,Mathias Rapacioli","doi":"10.1021/acs.jctc.6c01840","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01840","url":null,"abstract":"Abstract It is often challenging to model the evolution of the properties of molecular compounds when they interact with an environment. We will present the combination of the density functional tight binding (DFTB) scheme, describing molecules, with a force field (FF) approach, describing rare gas atoms. We have extended a former model [Iftneret al.J. Chem. Phys.2014,140, 03430110.1063/1.4861431. ] by introducing an atomic-population-dependent correction to a repulsion term between the DFTB atoms and rare gas atoms. In this new model, the dispersion contribution also depends on atomic populations. This model allows, in particular, describing neutral and ionic systems and has been applied to simulate clusters of argon atoms and a 1,4-diazabicyclo[2.2.2]octan (DABCO) molecule. New reference calculations (MP2, CCSD(T)-F12) on small DABCO- Arn=1,40/+ clusters are also reported. We show that the new DFTB-FF model performances are significantly improved with respect to the former model for small aggregates, in particular with regard to cohesive energies. Finally, by coupling the new DFTB-FF model with a minima global search algorithm, namely a combination of Parallel Tempering Monte Carlo explorations and local quenches, we provide a set of stable structures for DABCO- Arn0/+ clusters in the range n = 1–50 atoms pointing out the size evolution of geometric features and energetic properties. Ionization potentials are shown to decrease with size, consistent with new experimental measurements. This decrease is shown to undergo a transition after the completion of the first solvation shell.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"54 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151273","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}