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arXiv 2023年

作者： Yin, Zhuowen Ding, Xinyao Zhang, Xin Wu, Zhengwang Wang, Li Xu, Xiangmin Li, Gang School of Electronics and Information Engineering South China University of Technology Guangdong Province Guangzhou510641 China Department of Bioengineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Radiology Biomedical Research Imaging Center University of North Carolina Chapel Hill Chapel HillNC27599 United States The Affiliated Shenzhen School of Guangdong Experimental High School Guangdong Province Shenzhen518100 China School of Future Technology South China University of Technology Guangdong Province Guangzhou510641 China Pazhou Lab Guangdong Province Guangzhou510330 China

Autism Spectrum Disorder (ASD) has been emerging as a growing public health threat. Early diagnosis of ASD is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in ASD infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and we used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Tunable multicolor luminescence via energy transfer in CaLaGaO 4 :Tb 3+ , Eu 3+ single-phase phosphors

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Inorganic Chemistry Communications 2025年 179卷

作者： Ankoji Parvathala S.V. Prahbakar Vattikuti Mohammad Rezaul Karim Ramar Rajamanikandan Nam Nguyen Dang N. Suresh Kumar Department of Physics JNTUA College of Engineering Pulivendula Andhra Pradesh 516390 India School of Mechanical Engineering College of Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Center of Excellence for Research in Engineering Materials (CEREM) Deanship of Scientific Research King Saud University Riyadh 11421 Saudi Arabia Department of Physics and Semiconductor Sciences Gachon University Seongnam-si 13120 Republic of Korea Future Materials & Devices Lab. Institute of Fundamental and Applied Science Duy Tan University Ho Chi Minh City 70000 Viet Nam The Faculty of Environmental and Chemical Engineering Duy Tan University Danang 55000 Viet Nam Department of Physics JNTUA College of Engineering Anantapur Andhra Pradesh 515002 India

A multicolor tunable CaLaGaO 4 :Tb 3+ , Eu 3+ phosphors has been synthesized using typical solid-state reaction technique. The prepared powder samples were single phase tetragonal crystal structure confirmed by the XRD studies, SEM images shows the inhomogeneous morphological nature. The EDS confirmed the existence of precursor elements in prepared samples. Under excitation with 381 nm, the CaLaGaO 4 :Tb 3+ phosphors emission spectra shows characteristic emission peaks of Tb 3+ ion at wavelengths of 490 nm, 543 nm, 580 nm and 624 nm and the calculated CIE values were indicates the green emission. The CaLaGaO 4 :Tb 3+ , Eu 3+ co-doped phosphors stimulated under at a wavelength of 381 nm, the phosphors demonstrated emissions in green and red, which align with the characteristic wavelengths of Tb 3+ and Eu 3+ , respectively. Moreover, the emission spectral data and fluorescence decay lifetimes evinced the energy transfer in CaLaGaO 4 :Tb 3+ , Eu 3+ co-doped phosphors. Besides, at 0.5% doping concentration of Eu 3+ , the efficiency of energy transfer reached to 70.33% with 92.60% color purity. Furthermore, the mechanism of energy transfer from Tb 3+ to Eu 3+ in the CaLaGaO 4 phosphors was established to occur through dipole–dipole interchange. The increment in Eu 3+ ion concentration results in a color transition of prepared phosphors from green to yellow, and subsequently to red, attributed to the energy transfer mechanisms present. This phenomenon is ascribed due to the involvement of underlying energy transfer mechanisms. The outcomes demonstrated that the prepared CaLaGaO 4 :Tb 3+ , Eu 3+ phosphors are might be suitable candidates for multicolor tunable phosphors, with potential applications in lighting and display technologies.

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Two-dimensional materials for future information technology: status and prospects

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Science China(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng XU Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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Multicolor tunable luminescence in Dy3+/Eu3+ co-doped CaLaGaO4 phosphors for advanced lighting and display technologies

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Journal of Physics and Chemistry of Solids 2025年 207卷

作者： Parvathala, Ankoji Vattikuti, S.V. Prahbakar Karim, Mohammad Rezaul Kumar, N. Suresh Ravindran, Ezhakudiyan Rosaiah, P. Nguyen Dang, Nam Department of Physics JNTUA College of Engineering Pulivendula Andhra Pradesh 516390 India School of Mechanical Engineering College of Engineering Yeungnam University Gyeongsan38541 Korea Republic of Deanship of Scientific Research King Saud University Riyadh11421 Saudi Arabia Department of Physics JNTUA College of Engineering Anantapur Andhra Pradesh 515002 India Department of Chemistry SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai60323 India Thandalam Chennai602 105 India Future Materials & Devices Lab. Institute of Fundamental and Applied Science Duy Tan University Ho Chi Minh City70000 Viet Nam The Faculty of Environmental and Chemical Engineering Duy Tan University Danang55000 Viet Nam

Tunable multicolor luminescence Dy3+ and Eu3+ co-doped CaLaGaO4 phosphors are synthesis by a high-temperature solid-state reaction technique. The X-ray diffraction (XRD) studies confirmed the single phase tetragonal crystal structure for all the prepared samples, elemental and oxidation states are revealed by X-ray photoelectron spectroscopy (XPS) studies, FTIR studies confirmed the metallic and oxygen bonds existence in all the samples. CaLaGaO4: Dy3+ phosphor exhibits strong emission peaks at 484 nm (blue) and 576 nm (yellow) under excited at 388 nm, with 0.7% Dy3+ doping being the optimum concentration. In addition, the possibility of energy transfer is demonstrated by spectrum overlap analysis of Dy3+ and Eu3+ in CaLaGaO4: 0.7% Dy3+, y% Eu3+ co-doped phosphor. CaLaGaO4: Dy3+/Eu3+ co-doped phosphors are excited under 388 nm, the emission spectra is consist of emission peaks at 484 nm, 576 nm, 590 nm, 613 nm, 652 nm, and 701 nm, which are corresponding to Dy3+ and Eu3+ ions. Moreover, the emission spectral data and fluorescence decay lifetimes evinced the energy transfer in CaLaGaO4:Dy3+/Eu3+ co-doped phosphors. At 0.5% Eu3+, the energy transfer efficiency can reach 78%, and the reason is disclosed through energy transfer mechanism. By adjusting the doping of Eu3+ ion concentration in CaLaGaO4: 0.7% Dy3+, y% Eu3+, the CIE and associated color temperature can be adjusted for accomplishing tunable luminescence from cold yellowish white light to cold white and warm reddish-orange emitting light. In summary, outcomes demonstrated that the prepared CaLaGaO4: Dy3+/Eu3+ phosphors are viable candidates for multicolor tunable phosphors, with potential candidates for white light-emitting diodes (WLEDs) and display technologies. © 2025 Elsevier Ltd

关键词： Phosphorescence

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Correction: Ordered Co/Ni oxide nanostructures from MOFs: enhancing efficiency in hybrid asymmetric energy devices

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Journal of Materials Chemistry C 2025年第24期13卷 12564-12564页

作者： Xiaolong Leng S. V. Prabhakar Vattikuti Yumei Li P. Rosaiah Abdullah N. Alodhayb Saravanan Pandiaraj Burragoni Sravanthi Goud Ganesh Koyyada Jae Hong Kim Nam Nguyen Dang Jaesool Shim School of Mechanical Engineering Hubei Engineering University Xiaogan 432000 China Hubei Engineering Research Center for Key Technologies in Modern Paper and Sanitary Products Manufacturing Xiaogan 432000 China School of Mechanical Engineering College of Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Department of Physics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602105 Tamil Nadu India Department of Physics and Astronomy College of Science King Saud University Riyadh 11451 Saudi Arabia Department of Self-Development Skills King Saud University Riyadh Saudi Arabia School of Chemical Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Future Materials & Devices Lab. Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City 70000 Vietnam The Faculty of Environmental and Chemical Engineering Duy Tan University Danang 50000 Vietnam

Correction for ‘Ordered Co/Ni oxide nanostructures from MOFs: enhancing efficiency in hybrid asymmetric energy devices’ by Xiaolong Leng et al., J. Mater. Chem. C, 2025, 13, 9653–9666, https://***/10.1039/D5TC00922G.

Correction for ‘Ordered Co/Ni oxide nanostructures from MOFs: enhancing efficiency in hybrid asymmetric energy devices’ by Xiaolong Leng et al., J. Mater. Chem. C, 2025, 13, 9653–9666, https://***/10.1039/D5TC00922G.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Bio-futures for Transplanetary Habitats - A Summary and Key Outcomes from the 2022 Symposium 73

Bio-Futures for Transplanetary Habitats - A Summary and Key ...

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73rd International Astronautical Congress, IAC 2022

作者： van Ellen, Layla A. Belling, Anne-Sofie Lipińska, Monika Brandić Nerlich, Paula Azzopardi, Harry Ciardullo, Christina Dade-Robertson, Martyn Holt, Amy Hyry, Niina James, Paul MacCowan, Richard James Roberts, Aled Deakin Vermeulen, Angelo Zhang, Meng Hub for Biotechnology in the Built Environment Newcastle University Newcastle-Upon-TyneNE1 7RU United Kingdom ARUP Sydney Barrack Place 151 Clarence St SydneyNSW2000 Australia Yale Centre for Ecosystems in Architecture Yale School of Architecture 180 York Street New havenCT06511 United States Hub for Biotechnology in the Built Environment School of Architecture and Landscape Newcastle University Newcastle-Upon-TyneNE1 7RU United Kingdom International Space University 1 Rue Jean-Dominique Cassini Illkirch-Graffenstaden67400 France Leirikatu 7 C24 Espoo Finland Hub for Biotechnology in the Built Environment Department of Applied Sciences Northumbria University Newcastle-Upon-TyneNE1 8ST United Kingdom Biomimicry Innovation Lab Blake House 18 Blake Street YorkYO1 8QG United Kingdom Future Biomanufacturing Research Hub Manchester Institute of Biotechnology The University of Manchester ManchesterM1 7DN United Kingdom Systems Engineering and Simulation Faculty of Technology Policy and Management Delft University of Technology Jaffalaan 5 Delft2628 BX Netherlands

Bio-futures for Transplanetary Habitats (BFfTH) is a Special Interest Group within the Hub for Biotechnology in the Built Environment that aims to explore and enable interdisciplinary research on transplanetary habitats and habitats within extreme environments through an emphasis on the bio-social and biotechnological relations. BFfTH organized the online and onsite networking symposium Bio-futures for Transplanetary Habitats to examine how emerging biotechnologies, living materials, and more-than-human life can be implemented in habitat design and mission planning. The two day symposium aimed to serve as a catalyst in establishing an international network of collaborators across industry, academia and the private sector. It also aimed to support the development of novel methodologies to move beyond discipline-specific approaches in order to address and interrogate emerging questions surrounding potential transplanetary habitats and habitats in extreme environments. The symposium was divided into five sessions which hosted a minimum of three speakers each, these sessions were: Mycelium for Mars, Plants and Agriculture, Sustainable Habitats and Travels, Artistic Approach to Extremes Habitats, and Novel Biotechnologies for Space Habitats. This paper presents key outcomes from the symposium sessions, moderated panel, and informal discussions. The trends in ongoing research are identified and summarized following the use of biotechnology and bio-design to ensure and support safety, sustainability, habitability, reliability, crew efficiency, productivity and comfort in extreme environments both here on Earth and off-world. Moving beyond pure design and engineering innovation, the outcomes of this symposium also further interrogates sociotechnical imaginaries. Biodesign-based and biotechnologically-enabled transplanetary futures are investigated to understand how we want these futures to behave, feel and be experienced. The symposium hosted a wide range of topics includin

关键词： Biotechnology

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Annual research review of perovskite solar cells in 2023

材料展望（英文）

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材料展望（英文） 2024年第2期3卷 161-213页

作者： Qisen Zhou Xiaoxuan Liu Zonghao Liu Yanqing Zhu Jianfeng Lu Ziming Chen Canjie Li Jing Wang Qifan Xue Feifei He Jia Liang Hongyu Li Shenghao Wang Qidong Tai Yiqiang Zhang Jiehua Liu Chuantian Zuo Liming Ding Zhenghong Xiong Renhao Zheng Huimin Zhang Pengjun Zhao Xi Jin Pengfei Wu Fei Zhang Yan Jiang Huanping Zhou Jinsong Hu Yang Wang Yanlin Song Yaohua Mai Baomin Xu Shengzhong Liu Liyuan Han Wei Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074People's Republic of China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074People's Republic of China Optics Valley Laboratory Hubei 430074People's Republic of China State Key Laboratory of Silicate Materials for Architectures Wuhan University of TechnologyWuhan 430070People's Republic of China State Key Laboratory of Luminescent Materials and Devices Institute of Polymer Optoelectronic Materials and DevicesSchool of Materials Science and EngineeringSouth China University of TechnologyGuangzhou 510640People's Republic of China School of Materials and Energy Guangdong University of TechnologyGuangzhou 510006People's Republic of China Department of Materials Science State Key Laboratory of Photovoltaic Science and TechnologyFudan University220 Handan RoadShanghai 200433People's Republic of China Materials Genome Institute Shanghai UniversityShangda Rd.99Shanghai 200444People's Republic of China The Institute of Technological Sciences Wuhan UniversityWuhan 430072People's Republic of China College of Chemistry Zhengzhou UniversityZhengzhou 450001People's Republic of China Future Energy Laboratory School of Materials Science and EngineeringHefei University of TechnologyHefei 230009People's Republic of China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People's Republic of China School of Chemical Engineering Center for Antibonding Regulated CrystalsSungkyunkwan UniversitySuwon 16419Republic of Korea State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions Xinjiang Key Laboratory of Electronic Information Materials and Devices Xinjiang Technical Institute of Physics & ChemistryCAS40-1 South Beijing RoadUrumqi 830011People's Republic of China S

Perovskite(PVK)solar cells(PSCs)have garnered considerable research interest owing to their cost-effectiveness and high efficiency.A systematic annual review of the research on PSCs is essential for gaining a comprehensive understanding of the current research ***,systematic analysis of the research papers on PSCs reporting key findings in 2023 was *** on the results,the papers were categorized into six classifications,including regular n-i-p PSCs,inverted p-i-n PSCs,PVK-based tandem solar cells,PVK solar modules,device stability,and lead toxicity and green ***,a detailed overview and summary of the annual research advancements within each classification were ***,this review serves as a valuable resource for guiding future research endeavors in the field of PSCs.

关键词： perovskite solar cells annual review systematic review

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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