检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Guiding principle for minor-embedding in simulated-annealing-based ising machines

arXiv

引用

arXiv 2020年

作者： Shirai, Tatsuhiko Tanaka, Shu Togawa, Nozomu Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan

We propose a novel type of minor-embedding (ME) in simulated-annealing-based Ising machines. The Ising machines can solve combinatorial optimization problems. Many combinatorial optimization problems are mapped to find the ground (lowest-energy) state of the logical Ising model. When connectivity is restricted on Ising machines, ME is required for mapping from the logical Ising model to a physical Ising model, which corresponds to a specific Ising machine. Herein we discuss the guiding principle of ME design to achieve a high performance in Ising machines. We derive the proposed ME based on a theoretical argument of statistical mechanics. The performance of the proposed ME is compared with two existing types of MEs for different benchmarking problems. Simulated annealing shows that the proposed ME outperforms existing MEs for all benchmarking problems, especially when the distribution of the degree in a logical Ising model has a large standard deviation. This study validates the guiding principle of using statistical mechanics for ME to realize fast and high-precision solvers for combinatorial optimization problems. © 2020, CC BY.

关键词： Ising model

来源：评论

学校读者我要写书评

暂无评论

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

引用

arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Classification supporting Covid-19 diagnostics based on patient survey data

arXiv

引用

arXiv 2020年

作者： Henzela, Joanna Tobiasz, Joanna Kozielskia, Michal Bach, Malgorzata Foszner, Pawel Gruca, Aleksandra Kania, Mateusz Mika, Justyna Papiez, Anna Werner, Aleksandra Zyla, Joanna Jaroszewicz, Jerzy Polanska, Joanna Sikoraa, Marek Department of Computer Networks and Systems Silesian University of Technology Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland

Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARSCoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (***), which can serve as support in screening patients with COVID-19 disease. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set consisting of more than 3,000 examples is based on questionnaires collected at a hospital in Poland. Copyright © 2020, The Authors. All rights reserved.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic–English Code-Switching Levels Based on Character Profiles

International Journal of Asian Language Processing

引用

International Journal of Asian Language Processing 2022年第1期32卷

作者： Injy Hamed Alia El Bolock Cornelia Herbert Slim Abdennadher Ngoc Thang Vu https://***/0000-0002-9171-9461 Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Applied Emotional and Motivational Psychology Institute of Psychology and Education Faculty of Engineering Computer Science and Psychology Ulm University Ulm Germany Faculty of Informatics & Computer Science German International University Cairo Egypt Computer Science Department The German University in Cairo Cairo Egypt

Code-switching (CS) is a common linguistic phenomenon exhibited by multilingual individuals, where they tend to alternate between languages within one single conversation. CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behavior across speakers. Given that the factors giving rise to CS vary from one country to another, as well as from one person to another, CS is found to be a speaker-dependent behavior, where the frequency by which the foreign language is embedded differs across speakers. While several researchers have looked into predicting CS behavior from a linguistic point of view, research is still lacking in the task of predicting the user CS behavior from sociological and psychological perspectives. We provide an empirical user study, where we investigate the correlations between users’ CS levels and character traits. We conduct interviews with bilinguals and gather information on their profiles, including their demographics, personality traits, and traveling experiences. We then use machine learning (ML) to predict users’ CS levels based on their profiles, where we identify the main influential factors in the modeling process. We experiment with both classification as well as regression tasks. Our results show that the CS behavior is affected by the relation between speakers, travel experiences, as well as Neuroticism and Extraversion personality traits.

关键词： Code-switching Code-mixing Character Computing Personality Arabic–English Code-switching

来源：评论

学校读者我要写书评

暂无评论

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv

引用

arXiv 2021年

作者： Shoeibi, Afshin Sadeghi, Delaram Moridian, Parisa Ghassemi, Navid Heras, Jonathan Alizadehsani, Roohallah Khadem, Ali Kong, Yinan Nahavandi, Saeid Zhang, Yu-Dong Gorriz, Juan M. The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Deakin University VIC3217 Australia The School of Engineering Macquarie University Sydney2109 Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Informatics University of Leicester Leicester United Kingdom The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry. University of Cambridge United Kingdom

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1DCNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25. © 2021, CC BY-NC-ND.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

ParSimTech Research and Training Center

ParSimTech Research and Training Center

引用

IEEE European Technology and engineering Management Summit (E-TEMS)

作者： Oleksandr Miroshkin Sergiy Kovalov Olga Dmitrieva Oleksandr Shcherbakov Serhii Tsololo Yuliia Dikova Sergiy Gogolenko Volodymyr Kushnarenko Volodymyr Svyatnyy Communication and Information Centre Ulm University Ulm Germany Computer Engineering Department Donetsk National Technical University Pokrovsk Ukraine Department of Applied Mathematics and Informatics Donetsk National Technical University Pokrovsk Ukraine HLRS High-Performance Computing Center Stuttgart Stuttgart Germany

ISBN: (数字)9781728109039

ISBN: (纸本)9781728109046

A citizen's quality of life is always tightly related to the improvement of general technological progress in many subject areas - from mechanical and electrical engineering, information technology, mining and technological fields of industry up to biotechnological processes of cellular and molecular dynamics, medicine, and biomechanics of living organisms. It also depends on an achieved level in theory and mathematical description of the internal dynamic processes of different physical nature, simulation of these processes and further practical implementation of the theory into the real technical objects. An important role here plays computing resources which allow to minimize computational time and to increase the complexity of the simulation models. Parallel computing systems open for scientists a new level of simulation, but they also set high requirements on specific knowledge and experience of the users. As a result, granting access to computing resources is not sufficient, specialised training of the personnel is also needed. The same problem appears during the integration of a computing centre into the university educational program - new courses, workshops and learning materials are required. In this paper, we share our experience and a vision, how modern computing resources could be integrated into university life, what challenges and issues can be expected here. Also, we present a new scientific project between Ukrainian and German academia partners which has a goal to enhance accessibility, usability and improve the system organization of parallel computing resources. The project represents a multidisciplinary research area of Parallel Simulation Technology (ParSimTech) and focuses on the creation of the Ukrainian research and training ParSimTech centre.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Erratum to:Influence of structural defects on charge density waves in 1T-TaS_(2)

引用

Nano Research 2024年第3期17卷 2204-2204页

作者： Iaroslav Lutsyk Karol Szalowski Pawel Krukowski Pawel Dabrowski Maciej Rogala Witold Kozlowski Maxime Le Ster Michal Piskorski Dorota A.Kowalczyk Wojciech Rys Rafal Dunal Aleksandra Nadolska Klaudia Toczek Przemyslaw Przybysz Ewa Lacinska Johannes Binder Andrzej Wysmolek Natalia Olszowska Jacek J.Kolodziej Martin Gmitra Takuma Hattori Yuji Kuwahara Guang Bian Tai-Chang Chiang Pawel J.Kowalczyk Faculty of Physics and Applied Informatics University of LodzPomorska 149/15390-236 LodzPoland Faculty of Physics University of WarsawPasteura 502-093 WarsawPoland Faculty of Physics Astronomyand Applied Computer ScienceJagiellonian UniversityLojasiewicza 1130-348 KrakowPoland National Synchrotron Radiation Centre SOLARIS Jagiellonian UniversityCzerwone Maki 9830-392 KrakówPoland Institute of Physics Faculty of SciencePavol JozefŠafárik University in KošicePark Angelinum 904001 KošiceSlovakia Institute of Experimental Physics Slovak Academy of SciencesWatsonova 4704001 KošiceSlovakia Department of Precision Engineering Graduate School of EngineeringOsaka University2-1 Yamada-okaSuita 565-0871Japan Department of Physics and Astronomy University of MissouriColumbiaMissouri 65211USA Department of Physics and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois 61801-3080USA

The name of the second author in original paper was unfortunately *** should be“Karol Szalowski”,instead of“Karoli Szalowski”.

关键词： structural charge defects

来源：评论

学校读者我要写书评

暂无评论

Correction to: A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition

引用

Arabian Journal for Science and engineering 2022年 1-1页

作者： Khan, Muhammad Attique Zhang, Yu-Dong Alhaisoni, Majed Kadry, Seifedine Wang, Shui-Hua Saba, Tanzila Iqbal, Tassawar Department of Computer Science HITEC University Taxila Pakistan Department of Informatics University of Leicester Leicester UK College of Computer Science and Engineering University of Ha’il Ha’il Saudi Arabia Department of Applied Data Science Noroff University College Noroff Oslo Norway Department of Mathematics University of Leicester Lecicester UK College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Department of Computer Science COMSATS University Islamabad Wah Campus Islamabad Pakistan

A Correction to this paper has been published: 10.1007/s13369-021-05881-4

关键词：

来源：评论

学校读者我要写书评

暂无评论

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

arXiv

引用

arXiv 2023年

作者： Choudhary, Kamal Wines, Daniel Garrity, Kevin F. Williams, Maureen Tavazza, Francesca Li, Kangming Hattrick-Simpers, Jason Gupta, Vishu Romero, Aldo H. Krogel, Jaron T. Saritas, Kayahan Fuhr, Addis Ganesh, Panchapakesan Kent, Paul R.C. Yan, Keqiang Lin, Yuchao Ji, Shuiwang Blaiszik, Ben Reiser, Patrick Friederich, Pascal Agrawal, Ankit Tiwary, Pratyush Beyerle, Eric Minch, Peter Rhone, Trevor David Takeuchi, Ichiro Wexler, Robert B. Mannodi-Kanakkithodi, Arun Ertekin, Elif Mishra, Avanish Mathew, Nithin Wood, Mitchell Rohskopf, Andrew Dale Wang, Shih-Han Achenie, Luke E.K. Xin, Hongliang Biacchi, Adam J. Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Materials Science and Engineering University of Toronto 27 King’s College Cir TorontoON Canada Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Ludwig Institute for Cancer Research Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Globus University of Chicago IL60637 United States Data Science Learning Division National Lab ArgonneIL60439 United States Institute of Nanotechnology Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Department of Chemistry Institute of Materials Science and Engineering Washington University in St. Louis St. LouisMO63130 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Mechan

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://***/jarvis_leaderboard/ © 2023, CC BY.

关键词： Electronic structure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：