npj Computational Materials最新文献

Flexible uncertainty calibration for machine-learned interatomic potentials 机器学习原子间势的柔性不确定度校准

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-27 DOI: 10.1038/s41524-026-02080-3

Cheuk Hin Ho, Christoph Ortner, YangShuai Wang

{"title":"Flexible uncertainty calibration for machine-learned interatomic potentials","authors":"Cheuk Hin Ho, Christoph Ortner, YangShuai Wang","doi":"10.1038/s41524-026-02080-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02080-3","url":null,"abstract":"Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction intervals with guaranteed coverage under minimal assumptions, making it an attractive tool for UQ. However, existing CP techniques, while offering formal coverage guarantees, often lack accuracy, scalability, and adaptability to the complexity of atomic environments. In this work, we present a flexible uncertainty calibration framework for MLIPs, inspired by CP but reformulated as a parameterized optimization problem. This formulation enables the direct learning of environment-dependent quantile functions, producing sharper and more adaptive predictive intervals at negligible computational cost. Using the foundation model MACE-MP-0 as a representative case, we demonstrate the framework across diverse benchmarks, including ionic crystals, catalytic surfaces, and molecular systems. Our results achieve substantial improvements in uncertainty-error correlation, improve the detection of high-error configurations for active learning, and transfer reliably across distinct exchange-correlation functionals. Importantly, it is general, data efficient, and compatible with diverse MLIP architectures and baseline UQ schemes, offering a practical route toward robust and transferable atomistic simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"88 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy 使用机器学习的扫描透射电子显微镜在二维材料中自主制造定制缺陷结构

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-27 DOI: 10.1038/s41524-026-02025-w

Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh, Kai Xiao, Raymond R. Unocic, Stephen Jesse, Rama Vasudevan, Matthew G. Boebinger

{"title":"Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy","authors":"Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh, Kai Xiao, Raymond R. Unocic, Stephen Jesse, Rama Vasudevan, Matthew G. Boebinger","doi":"10.1038/s41524-026-02025-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02025-w","url":null,"abstract":"Materials with tailored quantum properties can be engineered from atomic-scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here, we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of MoS₂ monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring of structural evolution of defects. A machine learning framework combining a random forest model and a convolutional neural network (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to a localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties1–3, the proposed methods to build the autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond MoS₂.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis AQCat25：解锁多相催化的自旋感知、高保真机器学习潜力

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-27 DOI: 10.1038/s41524-026-02099-6

Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Rodrigo Wang, Shivang Agarwal, Alan Rask, Alexandre Fleury, Chuhong Wang, Andrew Wildman, Thomas Mustard, Kevin Ryczko, Paul Abruzzo, AJ Nish, Aayush R. Singh

{"title":"AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis","authors":"Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Rodrigo Wang, Shivang Agarwal, Alan Rask, Alexandre Fleury, Chuhong Wang, Andrew Wildman, Thomas Mustard, Kevin Ryczko, Paul Abruzzo, AJ Nish, Aayush R. Singh","doi":"10.1038/s41524-026-02099-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02099-6","url":null,"abstract":"Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a dataset of 13.5 million density functional theory (DFT) single-point calculations designed to enhance the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate integrating datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset’s knowledge. Conversely, joint training strategies prove effective for improving accuracy on new distributions without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example, by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances accuracy. Ultimately, we establish an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"152 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spin-Vibronic Coupling and Magnetic Blocking Temperature in High-Performance Single-Atom Magnet NpO@MgO 高性能单原子磁体的自旋-振动耦合和磁阻塞温度NpO@MgO

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-25 DOI: 10.1038/s41524-026-02081-2

Jie Liu, Ninghan Qiao, Guanqi Wang, Hong Cui, Hong Chen, Ruizhi Qiu, Hongkuan Yuan

{"title":"Spin-Vibronic Coupling and Magnetic Blocking Temperature in High-Performance Single-Atom Magnet NpO@MgO","authors":"Jie Liu, Ninghan Qiao, Guanqi Wang, Hong Cui, Hong Chen, Ruizhi Qiu, Hongkuan Yuan","doi":"10.1038/s41524-026-02081-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02081-2","url":null,"abstract":"Actinide-based single-atom magnet (SAM) represents a promising platform for ultrahigh-density magnetic data storage. However, their performance is fundamentally constrained by low effective barriers (Ueff) and blocking temperature (TB). Traditional design focuses on maximizing static magnetic anisotropy, often overlooking how spin-vibronic coupling governs Ueff and TB. Through integrated multi-level calculations and ab initio spin dynamics simulation, we demonstrate a paradigm shift: spin-vibronic coupling, not spin levels, dictates performance. The apical oxygen coordination transforms Np@MgO (Ueff = 46.3 meV) into the high-performance system NpO@MgO, yielding an intrinsic high barrier (Ueff = 329.4 meV) and a prolonged quantum-tunneling time (τQTM =2.22 s). Crucially, strong spin-vibronic coupling with a low-energy twisting mode at 8.17 meV drives efficient two-phonon Raman relaxation below 32 K, thereby reducing Ueff to merely 8.17 meV. Despite this dynamic bottleneck, NpO@MgO maintains a high TB of 50 K. Our work establishes that suppressing detrimental low-energy vibrational modes, rather than solely optimizing static anisotropy Ueff, is essential for advancing SAM performance, thereby introducing a “spin-phonon landscape engineering” paradigm for future design of molecular nanomagnets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A structural signature linking local atomic packing to holistic structure in metallic glasses 金属玻璃中连接局部原子填料和整体结构的结构特征

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-25 DOI: 10.1038/s41524-026-02100-2

Yitao Sun, Chao Wang, Mingxing Li, Fucheng Li, Maozhi Li, Yanhui Liu, Weihua Wang

{"title":"A structural signature linking local atomic packing to holistic structure in metallic glasses","authors":"Yitao Sun, Chao Wang, Mingxing Li, Fucheng Li, Maozhi Li, Yanhui Liu, Weihua Wang","doi":"10.1038/s41524-026-02100-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02100-2","url":null,"abstract":"The disordered atomic arrangement of amorphous materials presents fundamental challenges to structural characterization. Similar to how elemental crystals offer basic structure templates for the understanding of crystal lattices, the structure signatures of elemental metallic glasses might also offer insights on how atoms are packed in amorphous systems. Here, we created an elemental metallic glasses database containing 51 elements via ab initio molecular dynamics simulations. By analyzing their structures through various descriptors, we discovered an unexpected correlation between the local 5-fold symmetry and the second peak width in radial distribution function. The correlation, offering a universal structural signature linking local atomic packing to holistic amorphous structure, was found to originate from the bimodal distribution of interatomic distances in the second coordination shell. Guided by this correlation, we successfully prepared Re elemental metallic glass using conventional magnetron sputtering deposition. The structural signature revealed by the elemental metallic glasses will shed new light on the understanding of glassy structure and behaviors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties 提高时变密度泛函理论计算动力响应特性的效率

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-25 DOI: 10.1038/s41524-026-02088-9

Zhandos A. Moldabekov, Sebastian Schwalbe, Uwe Hernandez Acosta, Thomas Gawne, Jan Vorberger, Michele Pavanello, Tobias Dornheim

{"title":"Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties","authors":"Zhandos A. Moldabekov, Sebastian Schwalbe, Uwe Hernandez Acosta, Thomas Gawne, Jan Vorberger, Michele Pavanello, Tobias Dornheim","doi":"10.1038/s41524-026-02088-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02088-9","url":null,"abstract":"X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"33 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extraction of the self energy and Eliashberg function from angle resolved photoemission spectroscopy using the xARPES code 利用xARPES代码从角度分辨光发射光谱中提取自能和Eliashberg函数

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-24 DOI: 10.1038/s41524-026-02026-9

Thomas P. van Waas, Christophe Berthod, Jan Berges, Nicola Marzari, J. Hugo Dil, Samuel Poncé

{"title":"Extraction of the self energy and Eliashberg function from angle resolved photoemission spectroscopy using the xARPES code","authors":"Thomas P. van Waas, Christophe Berthod, Jan Berges, Nicola Marzari, J. Hugo Dil, Samuel Poncé","doi":"10.1038/s41524-026-02026-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02026-9","url":null,"abstract":"Angle-resolved photoemission spectroscopy is a powerful experimental technique for studying anisotropic many-body interactions through the electron spectral function. Existing attempts to decompose the spectral function into non-interacting dispersions and electron-phonon, electron-electron, and electron-impurity self-energies rely on linearization of the bands and manual assignment of self-energy magnitudes. Here, we show how self-energies can be extracted consistently for curved dispersions. We extend the maximum-entropy method to Eliashberg-function extraction with Bayesian inference, optimizing the parameters describing the dispersions and the magnitudes of electron-electron and electron-impurity interactions. We compare these novel methodologies with state-of-the-art approaches on model data, then demonstrate their applicability with two high-quality experimental data sets. With the first set, we identify the phonon modes of a two-dimensional electron liquid on TiO2-terminated SrTiO3. With the second set, we obtain unprecedented agreement between two Eliashberg functions of Li-doped graphene extracted from separate dispersions. We release these functionalities in the novel Python code XARPES.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-loop workflow of high-entropy materials discovery: efficient and accurate synthesizability prediction via domain-specific local LLMs 高熵材料发现的闭环工作流程：基于特定领域的局部llm的高效准确的可合成性预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-24 DOI: 10.1038/s41524-026-02092-z

Yeongjun Yoon, Geun Ho Gu, Kyeounghak Kim

{"title":"Closed-loop workflow of high-entropy materials discovery: efficient and accurate synthesizability prediction via domain-specific local LLMs","authors":"Yeongjun Yoon, Geun Ho Gu, Kyeounghak Kim","doi":"10.1038/s41524-026-02092-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02092-z","url":null,"abstract":"High-entropy materials (HEMs) offer unprecedented opportunities for superior mechanical, thermal, and catalytic properties, but their vast chemical space makes experimental discovery resource-intensive. State-of-the-art commercial large language models (LLMs) notably fail at HEM synthesizability prediction, a critical bottleneck in materials development. We demonstrate that domain-specific fine-tuning transforms open-weight local LLMs into accurate predictors. Using a dataset of 321,083 inorganic compositions with 2506 HEM examples, we fine-tuned three 4-bit-quantized models (gpt-oss-20b, Qwen3-14b, and DeepSeek-R1-Distill-Qwen-14b), achieving remarkable Matthews Correlation Coefficient (MCC) of 0.860, 0.875, and 0.868, respectively. Critically, these models operate efficiently on accessible hardware (< 15GB VRAM), eliminating costly API dependencies while ensuring data privacy and consistent reproducibility. This work could open new pathways toward autonomous closed-loop discovery, where distributed local models enable rapid screening and iterative improvement through experimental feedback. Future collaborative efforts in open data sharing, particularly including negative results, would address current fragmentation in synthesis reporting and accelerate community-wide HEM discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Valley polarization of graphene via the saddle point 经鞍点的石墨烯谷极化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-24 DOI: 10.1038/s41524-026-02096-9

Deepika Gill, Sangeeta Sharma, Peter Elliott, Kay Dewhurst, Sam Shallcross

引用次数: 0

Exploring charge density waves in two-dimensional NbSe2 with machine learning 利用机器学习探索二维NbSe2中的电荷密度波

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-24 DOI: 10.1038/s41524-026-02063-4

Norma Rivano, Francesco Libbi, Chuin Wei Tan, Christopher T. S. Cheung, Jose L. Lado, Arash A. Mostofi, Philip Kim, Johannes Lischner, Adolfo O. Fumega, Boris Kozinsky, Zachary A. H. Goodwin

{"title":"Exploring charge density waves in two-dimensional NbSe2 with machine learning","authors":"Norma Rivano, Francesco Libbi, Chuin Wei Tan, Christopher T. S. Cheung, Jose L. Lado, Arash A. Mostofi, Philip Kim, Johannes Lischner, Adolfo O. Fumega, Boris Kozinsky, Zachary A. H. Goodwin","doi":"10.1038/s41524-026-02063-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02063-4","url":null,"abstract":"Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs—capturing effects such as layer number, twist angle, and strain—remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe2. We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe2 and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0