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arXiv 2024年

作者： Zhang, Jun-Jie Song, Jiahao Wang, Xiu-Cheng Li, Fu-Peng Liu, Zehan Chen, Jian-Nan Dang, Haoning Wang, Shiyao Zhang, Yiyan Xu, Jianhui Shi, Chunxiang Wang, Fei Pang, Long-Gang Cheng, Nan Zhang, Weiwei Zhang, Duo Meng, Deyu Northwest Institute of Nuclear Technology No. 28 Pingyu Road Shaanxi Xi’an710024 China School of Telecommunications Engineering Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China State Key Laboratory of ISN No. 2 South Taibai Road Shaanxi Xi’an710071 China Institute of Particle Physics Central China Normal University No. 152 Luoyu Road Hubei Wuhan30079 China School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Guangzhou Institute of Geography Academy of Sciences No. 100 Xianlie Road Guangdong Guangzhou510070 China National Meteorological Information Center Beijing100044 China MDX Research Center for Element Strategy Institute of Integrated Research Institute of Science Tokyo Midori-ku Yokohama226-8503 Japan School of Physics and Information Technology Shaanxi Normal University No. 620 West Chang’an Avenue Shaanxi Xi’an710119 China School of Aeronautics Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Mathematics and Statistics Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas—weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication—we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security. © 2024, CC0.

关键词： Weather forecasting

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Atomically Dispersed Iron-Copper Dual-Metal Sites Synergistically Boost Carbonylation of Methane

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Angewandte Chemie 2024年第43期136卷

作者： Dr. Qingpeng Cheng Dr. Xueli Yao Prof. Dr. Guanna Li Guanxing Li Prof. Dr. Lirong Zheng Dr. Kaijie Yang Dr. Abdul-Hamid Emwas Prof. Dr. Xingang Li Prof. Dr. Yu Han Prof. Dr. Jorge Gascon KAUST Catalysis Center (KCC) Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Biobased Chemistry and Technology Wageningen University & Research Bornse Weilanden 9 Wageningen 6708WG The Netherlands Advanced Membranes and Porous Materials Center (AMPMC) KAUST Thuwal 23955-6900 Saudi Arabia Beijing Synchrotron Radiation Facility Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Imaging and Characterization Core Lab KAUST Thuwal 23955-6900 Saudi Arabia State Key Laboratory of Chemical Engineering Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Haihe Laboratory of Sustainable Chemical Transformations Tianjin Key Laboratory of Applied Catalysis Science and Engineering School of Chemical Engineering & Technology Tianjin University Tianjin 300350 P. R. China Electron Microscopy Center School of Emergent Soft Matter South China University of Technology Guangzhou 510640 P. R. China

The direct liquid-phase oxidative carbonylation of methane, utilizing abundant natural gas, offers a mild and straightforward alternative. However, most catalysts proposed for this process suffer from low acetic acid yields due to few active sites and rapid C1 oxygenate generation, impeding their industrial feasibility. Herein, we report a highly efficient 0.1Cu/Fe-HZSM-5-TF (TF denotes template-free synthesis) catalyst featuring exclusively mononuclear Fe and Cu anchored in the ZSM-5 channels. Under optimized conditions, the catalyst achieved an unprecedented acetic acid yield of 40.5 mmol g cat −1 h −1 at 50 °C, tripling the previous records of 12.0 mmol g cat −1 h −1 . Comprehensive characterization, isotope-labeled experiments and density functional theory (DFT) calculations reveal that the homogeneous mononuclear Fe sites are responsible for the activation and oxidation of methane, while the neighboring Cu sites play a key role in retarding the oxidation process, promoting C−C coupling for effective acetic acid synthesis. Furthermore, the methyl-group carbon in acetic acid originates solely from methane, while its carbonyl-group carbon is derived exclusively from CO, rather than the conversion of other C1 oxygenates. The proposed bimetallic catalyst design not only overcomes the limitations of current catalysts but also generalizes the oxidative carbonylation of other alkanes, demonstrating promising advancements in sustainable chemical synthesis.

关键词： Methane oxidation Carbonylation Zeolites C1 chemistry synergy

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Smart Robots: Self‐Propelled 3D‐Printed “Aircraft Carrier” of Light‐Powered Smart Micromachines for Large‐Volume Nitroaromatic Explosives Removal (Adv. Funct. Mater. 39/2019)

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advanced Functional Materials 2019年第39期29卷

作者： Lei Kong Adriano Ambrosi Muhammad Zafir Mohamad Nasir Jianguo Guan Martin Pumera Division of Chemistry and Biological Chemistry School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology 122 Luoshi road Wuhan 430070 P. R. China Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 166 28 Prague 6 Czech Republic Future Energy and Innovation Lab Central European Institute of Technology Brno University of Technology Purkyňova 656/123 Brno CZ‐616 00 Czech Republic Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei‐ro Seodaemun‐gu Seoul 03722 Korea

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Stable Interface Chemistry and Multiple Ion Transport of Composite Electrolyte Contribute to Ultra-long Cycling Solid-state LiNi0.8Co0.1Mn0.1O2/Lithium Metal Batteries

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Angewandte Chemie 2021年第46期133卷

作者： Ke Yang Likun Chen Jiabin Ma Dr. Chen Lai Dr. Yanfei Huang Jinshuo Mi Jie Biao Danfeng Zhang Peiran Shi Heyi Xia Prof. Guiming Zhong Prof. Feiyu Kang Prof. Yan-Bing He Shenzhen All-Solid-State Lithium Battery Electrolyte Engineering Research Center Institute of Materials Research (IMR) Tsinghua Shenzhen International Graduate School Shenzhen 518055 P. R. China School of Materials Science and Engineering Tsinghua University Beijing 100084 P. R. China Key Lab of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing 100084 P. R. China College of Materials Science and Engineering Shenzhen University Shenzhen 518055 P. R. China Laboratory of Advanced Spectro-electrochemistry and Li-ion Batteries Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 P. R. China

Severe interfacial side reactions of polymer electrolyte with LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) cathode and Li metal anode restrict the cycling performance of solid-state NCM811/Li batteries. Herein, we propose a chemically stable ceramic-polymer-anchored solvent composite electrolyte with high ionic conductivity of 6.0×10 −4 S cm −1 , which enables the solid-state NCM811/Li batteries to cycle 1500 times. The Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 nanowires (LNs) can tightly anchor the essential N, N-dimethylformamide (DMF) in poly(vinylidene fluoride) (PVDF), greatly enhancing its electrochemical stability and suppressing the side reactions. We identify the ceramic-polymer-liquid multiple ion transport mechanism of the LNs-PVDF-DMF composite electrolyte by tracking the 6 Li and 7 Li substitution behavior via solid-state NMR. The stable interface chemistry and efficient ion transport of LNs-PVDF-DMF contribute to superior performances of the solid-state batteries at wide temperature range of −20–60 °C.

关键词： composite electrolytes interface chemistry multiple ion transport solid-state lithium metal batteries

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Graphene Structure: Unconventional Atomic Structure of Graphene Sheets on Solid Substrates (Small 42/2019)

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Small 2019年第42期15卷

作者： Jinjin Zhang Yizhou Yang Shuo Yang Jie Song Ying Wang Xiaoguo Liu Qingqing Yang Yue Shen Shuo Wang Haijun Yang Junhong Lü Bin Li Haiping Fang Ratnesh Lal Daniel M. Czajkowsky Jun Hu Guosheng Shi Yi Zhang Zhangjiang Lab Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Key Laboratory of Interfacial Physics and Technology Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China Shanghai Applied Radiation Institute Shanghai University Shanghai 200444 China Institute of Nano Biomedicine and Engineering Shanghai Engineering Research Centre for Intelligent Diagnosis and Treatment Instrument Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Materials Science and Engineering University of California San Diego La Jolla CA 92093 USA Key Laboratory of Salt Lake Resources Chemistry of Qinghai Province Qinghai Institute of Salt Lakes Chinese Academy of Sciences Xining Qinghai 810008 China State Key Laboratory for Oncogenes and Bio-ID Center School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200240 China

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:Hydrogen Doping Oxide Transistors: Analysis of Ultrahigh Apparent Mobility in Oxide Field-Effect Transistors (Adv. Sci. 7/2019)

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advanced Science 2019年第7期6卷 1970040-1970040页

作者： Changdong Chen Bo-Ru Yang Gongtan Li Hang Zhou Bolong Huang Qian Wu Runze Zhan Yong-Young Noh Takeo Minari Shengdong Zhang Shaozhi Deng Henning Sirringhaus Chuan Liu State Key Lab of Opto-Electronic Materials & Technologies Guangdong Province Key Lab of Display Material and Technology School of Electronics and Information Technology Shunde International Joint Research Institute Sun Yat-Sen University Guangdong 510275 China Shenzhen Key Lab of Thin Film Transistor and Advanced Display Peking University Shenzhen Graduate School Peking University Shenzhen 518055 China Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong SAR The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518057 China Department of Energy and Materials Engineering Dongguk University 30 Pildong-ro 1 gil Jung-gu Seoul 04620 Republic of Korea International Center for Materials Nanoarchitectonics (WPI-MANA) National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305-0044 Japan Department of Physics University of Cambridge Cambridge CB3 1HK UK

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Retraction Note: Cu-loaded C3N4-MgO nanorods for promising antibacterial and dye degradation

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Applied Nanoscience 2024年第4期14卷 735-735页

作者： Akbar, M. U. Ikram, M. Imran, M. Haider, A. Ul-Hamid, A. Dilpazir, S. Shahzadi, I. Nazir, G. Shahzadi, A. Nabgan, W. Haider, J. Solar Cell Applications Research Lab Department of Physics Government College University Lahore Pakistan State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Centre for Soft Matter Science and Engineering Beijing Engineering Center for Hierarchical Catalysts Beijing University of Chemical Technology Beijing China Faculty of Veterinary and Animal Sciences Muhammad Nawaz Shareef University of Agriculture Multan Pakistan Core Research Facilities King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing People’s Republic of China Punjab University College of Pharmacy University of the Punjab Lahore Pakistan Department of Nanotechnology and Advanced Materials Engineering Sejong University Seoul Republic of Korea Faculty of Pharmacy University of the Lahore Lahore Pakistan School of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Malaysia Departament d’Enginyeria Química Universitat Rovira I Virgili Tarragona Spain Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin China

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Enhanced Acidic Water Oxidation by Dynamic Migration of Oxygen Species at the Ir/Nb2O5−xCatalyst/Support Interfaces

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Angewandte Chemie 2022年第52期134卷

作者： Zhaoping Shi Ji Li Jiadong Jiang Yibo Wang Xian Wang Yang Li Liting Yang Yuyi Chu Jingsen Bai Jiahao Yang Jing Ni Prof. Ying Wang Prof. Lijuan Zhang Prof. Zheng Jiang Prof. Changpeng Liu Prof. Junjie Ge Prof. Wei Xing State Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin 130022 P. R. China School of Applied Chemistry and Engineering University of Science and Technology of China Hefei Anhui 230026 P. R. China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201204 P. R. China University of Chinese Academy of Sciences Beijing 100049 P. R. China Key Laboratory of Physics and Technology for Advanced Batteries Ministry of Education College of Physics Jilin University Changchun 130012 P. R. China State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun 130022 P. R. China Shanghai Synchrotron Radiation Facility Zhangjiang National Lab Shanghai Advanced Research Institute Chinese Academy of Science Shanghai 201204 China

Catalyst/support interaction plays a vital role in catalysis towards acidic oxygen evolution (OER), and the performance reinforcement is currently interpreted by either strain or electron donation effect. We herein report that these views are insufficient, where the dynamic evolution of the interface under potential bias must be considered. Taking Nb 2 O 5− x supported iridium (Ir/Nb 2 O 5− x ) as a model catalyst, we uncovered the dynamic migration of oxygen species between IrO x and Nb 2 O 5− x during OER. Direct spectroscopic evidence combined with theoretical computation suggests these migrations not only regulate the in situ Ir structure towards boosted activity, but also suppress its over-oxidation via spontaneously delivering excessive oxygen from IrO x to Nb 2 O 5− x . The optimized Ir/Nb 2 O 5− x thus demonstrated exceptional performance in scalable water electrolyzers, i.e., only need 1.839 V to attain 3 A cm −2 (surpassing the DOE 2025 target), and no activity decay during a 2000 h test at 2 A cm −2 .

关键词： Acidic OER Dynamic Catalyst/Support Interface High Performance PEMWE Stability

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Tuning Intermediates Adsorption and C─N Coupling for Efficient Urea Electrosynthesis Via Doping Ni into Cu

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Small methods 2024年第3期8卷 e2300811页

作者： Yangyang Zhang Yajun Zhao Marshet Getaye Sendeku Fuhua Li Jinjie Fang Yuan Wang Zhongbin Zhuang Yun Kuang Bin Liu Xiaoming Sun State Key Lab of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 P. R. China. School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore 639798 Singapore. Ocean Hydrogen Energy R&D Center Research Institute of Tsinghua University in Shenzhen Shenzhen 518057 P. R. China. Department of Materials Science and Engineering City University of Hong Kong China Hong Kong SAR 999077 P. R. China.

Simultaneous electrochemical reduction of nitrite and carbon dioxide (CO ) under mild reaction conditions offers a new sustainable and low-cost approach for urea synthesis. However, the development of urea electrosynthesis thus far still suffers from low selectivity due to the high energy barrier of CO formation and the subsequent C─N coupling. In this work, a highly active dendritic Cu Ni catalyst is developed to enable the highly selective electrosynthesis of urea from co-reduction of nitrite and CO , reaching a urea Faradaic efficiency (FE) and production rate of 39.8% and 655.4 µg h cm , respectively, at -0.7 V versus reversible hydrogen electrode (RHE). In situ Fourier-transform infrared spectroscopy (FT-IR) measurements together with density functional theory (DFT) calculations demonstrate that Ni doping into Cu can significantly enhance the adsorption energetics of the key reaction intermediates and facilitate the C─N coupling. This work not only provides a new strategy to design efficient electrocatalysts for urea synthesis but also offers deep insights into the mechanism of C─N coupling during the co-reduction of nitrite and CO .

关键词： CO2 reduction C─N coupling Ni doping nitrite reduction urea synthesis

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