检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Tezak, Cooper Clary, Jacob Gerits, Sophie Quinton, Joshua Rich, Benjamin Singstock, Nicholas Alherz, Abdulaziz Aubry, Taylor Clark, Struan Hurst, Rachel Ben, Mauro Del Sutton, Christopher Sundararaman, Ravishankar Musgrave, Charles Vigil-Fowler, Derek Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Materials Chemical and Computational Science Directorate National Renewable Energy Laboratory GoldenCO80401 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Chemistry University of Colorado Boulder BoulderCO80309 United States Department of Mechanical Engineering University of Colorado Boulder BoulderCO80309 United States Department of Chemical Engineering College of Engineering and Petroleum Kuwait University Safat13060 Kuwait Applied Mathematics & Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Chemistry and Biochemistry University of South Carolina ColumbiaSC29208 United States Department of Materials Science and Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Renewable and Sustainable Energy Institute University of Colorado Boulder BoulderCO80309 United States Department of Chemical Engineering University of Utah Salt Lake CityUT84112 United States

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogenous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: Lithospheric foundering and underthrusting imaged beneath Tibet

引用

Nature communications 2018年第1期9卷 3443页

作者： Min Chen Fenglin Niu Jeroen Tromp Adrian Lenardic Cin-Ty A Lee Wenrong Cao Julia Ribeiro 318 Keith-Wiess Geology Lab Department of Earth Science Rice University MS 126 6100 Main Street Houston TX 77005 USA. Min.Chen@rice.edu. 318 Keith-Wiess Geology Lab Department of Earth Science Rice University MS 126 6100 Main Street Houston TX 77005 USA. State Key Laboratory of Petroleum Resource and Prospecting and Unconventional Natural Gas Institute China University of Petroleum Beijing 102249 China. Department of Geosciences Princeton University Princeton New Jersey 08544 USA. Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA.

The original version of the Supplementary Information associated with this Article contained an error in Supplementary Figure 4 in which the colours on the maps rendered incorrectly. The HTML has been updated to inclu...

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

引用

arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Real-space investigation of short-range magnetic correlations in uoride pyrochlores NaCaCo2F7 and NaSrCo2F7 with magnetic pair distribution function analysis

arXiv

引用

arXiv 2017年

作者： Frandsen, Benjamin A. Ross, Kate A. Krizan, Jason W. Nilsen, Goran J. Wildes, Andrew R. Cava, Robert J. Birgeneau, Robert J. Billinge, Simon J.L. Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Department of Physics Colorado State University Fort CollinsCO80523 United States Quantum Materials Program Canadian Institute for Advanced Research TorontoONM5G1Z8 Canada Department of Physics Princeton University PrincetonNJ08544 United States Institut Laue-Langevin CS 20156 Grenoble Cedex 938042 France ISIS Neutron and Muon Facility Rutherford Appleton Laboratory DidcotOX110QX United Kingdom Institut Laue-Langevin Grenoble38000 France Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Condensed Matter Physics and Materials Science Department Brookhaven National Laboratory UptonNY11973 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States

We present time-of-ight neutron total scattering and polarized neutron scattering measurements of the magnetically frustrated compounds NaCaCo2F7 and NaSrCo2F7, which belong to a class of recently discovered pyrochlore compounds based on transition metals and uorine. The magnetic pair distribution function (mPDF) technique is used to analyze and model the total scattering data in real space. We find that a previously-proposed model of short-range XY-like correlations with a length scale of 10-15 A, combined with nearest-neighbor collinear antiferromagnetic correlations, accurately describes the mPDF data at low temperature, confirming the magnetic ground state in these materials. This model is further verified by the polarized neutron scattering data. From an analysis of the temperature dependence of the mPDF and polarized neutron scattering data, we find that short-range correlations persist on the nearest-neighbor length scale up to 200 K, approximately two orders of magnitude higher than the spin freezing temperatures of these compounds. These results highlight the opportunity presented by these new pyrochlore compounds to study the effects of geometric frustration at relatively high temperatures, while also advancing the mPDF technique and providing a novel opportunity to investigate a genuinely short-range-ordered magnetic ground state directly in real space. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetism

来源：评论

学校读者我要写书评

暂无评论

Preface of the symposium: “V-International Symposium of computational and Mathematical Modeling of Biologic and Medicine Targets”

引用

AIP Conference Proceedings 2018年第1期2040卷

作者： Dilson Silva Celia M. Cortez 1Applied Mathematics Department Post-graduation Program in Computational Sciences and Post-graduation Program in Medical Sciences University of the State of Rio de Janeiro. Rua São Francisco Xavier 524 sala 6020 D 20550-900 - Rio de Janeiro / Brazil. Phone +55(21)34960298 Fax +55(21)34960298

Dilson Silva, Celia M. Cortez; Preface of the symposium: “V-International Symposium of computational and Mathematical Modeling of Biologic and Medicine Targets”

关键词：

来源：评论

学校读者我要写书评

暂无评论

Accelerating Giant Impact Simulations with Machine Learning

arXiv

引用

arXiv 2024年

作者： Lammers, Caleb Cranmer, Miles Hadden, Sam Ho, Shirley Murray, Norman Tamayo, Daniel Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Physics Center for Data Science New York University 726 Broadway New YorkNY10003 United States Department of Physics University of Toronto 60 St. George Street TorontoONM5S 1A7 Canada Department of Physics Harvey Mudd College ClaremontCA91711 United States

Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 N-body simulations of three-planet systems, we develop an ML model that can accurately predict which two planets will experience a collision, along with the state of the post-collision planets, from a short integration of the system's initial conditions. Our model greatly improves on non-ML baselines that rely on metrics from dynamics theory, which struggle to accurately predict which pair of planets will experience a collision. By combining with a model for predicting long-term stability, we create an ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with reasonable accuracy and a speedup of up to four orders of magnitude. We expect our model to enable analyses that would not otherwise be computationally feasible. As such, we release our training code, along with an easy-to-use API for our collision outcome model and giant impact emulator. Copyright © 2024, The Authors. All rights reserved.

关键词： Exoplanets

来源：评论

学校读者我要写书评

暂无评论

Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields

arXiv

引用

arXiv 2024年

作者： Barton, Brandon Sagal, Jacob Feeney, Sean Grattan, George Patnaik, Pratik Oganesyan, Vadim Carr, Lincoln D. Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Physics and Astronomy College of Staten Island City University of New York Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center City University of New York New YorkNY10016 United States

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization. © 2024, CC BY.

关键词： Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

The 230 GHz Variability of Numerical Models of Sagittarius A* I. Parameter Surveys on Varying Ion-electron Temperature Ratios Under Strongly Magnetized Conditions

arXiv

引用

arXiv 2023年

作者： Chan, Ho-Sang Chan, Chi-Kwan Prather, Ben S. Wong, George N. Gammie, Charles Department of Physics Institute of Theoretical Physics The Chinese University of Hong Kong N.T. Shatin Hong Kong JILA University of Colorado National Institute of Standards and Technology 440 UCB BoulderCO80309-0440 United States Department of Astrophysical and Planetary Sciences University of Colorado 391 UCB BoulderCO80309 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Los Alamos National Lab Los AlamosNM87545 United States School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Department of Physics University of Illinois at Urbana-Champaign 1110 West Green Street UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NCSA University of Illinois at Urbana-Champaign 1205 W. Clark St. UrbanaIL61801 United States Illinois Center for the Advanced Study of the Universe University of Illinois at Urbana-Champaign 1110 West Green St. UrbanaIL61801 United States

The 230 GHz lightcurves of Sagittarius A* (Sgr A*) predicted by general relativistic magnetohydrodynamics (GRMHD) and ray-tracing (GRRT) models in Event Horizon Telescope Collaboration et al. (2022) have higher variability M∆T compared to observations. In this series of papers, we explore the origin of such large brightness variability. In this first paper, we performed large GRRT parameter surveys that span from the optically thin to the optically thick regimes, covering the ion-electron temperature ratio under strongly magnetized conditions, RLow, from 1 to 60. We find that increasing RLow can lead to either an increase or a reduction in M∆T depending on other model parameters, making it consistent with the observed variability of Sgr A* in some cases. Our analysis of GRRT image snapshots finds that the major contribution to the large M∆T for the RLow = 1 models comes from the photon rings. However, secondary contributions from the accretion flow are also visible depending on the spin parameter. Our work demonstrates the importance of the electron temperature used for modelling radiatively inefficient accretion flows and places new constraints on the ion-electron temperature ratio. A more in-depth analysis for understanding the dependencies of M∆T on RLow will be performed in subsequent papers. © 2023, CC BY.

关键词： Black holes

来源：评论

学校读者我要写书评

暂无评论

Machine learning spectral indicators of topology

arXiv

引用

arXiv 2020年

作者： Andrejevic, Nina Andrejevic, Jovana Bernevig, B. Andrei Regnault, Nicolas Han, Fei Fabbris, Gilberto Nguyen, Thanh Drucker, Nathan C. Rycroft, Chris H. Li, Mingda Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Quantum Measurement Group Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics University of Pennsylvania PhiladelphiaPA19104 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics Princeton University PrincetonNJ08544 United States Donostia International Physics Center P. Manuel de Lardizabal 4 Donostia-San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Plaza Euskadi 5 Bilbao48009 Spain Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Department of Mathematics University of Wisconsin-Madison MadisonWI53706 United States Computational Research Division Lawrence Berkeley Laboratory BerkeleyCA94720 United States

Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials’ topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique sensitive to atoms’ local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, we leverage computed X-ray absorption near-edge structure (XANES) spectra of more than 10,000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F1 scores of 89% and 93% for topological and trivial classes, respectively. Additionally, we obtain consistent classifications using corresponding experimental and computational XANES spectra for a small number of measured compounds. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions. Copyright © 2020, The Authors. All rights reserved.

关键词： X ray absorption spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Confirming the calibration of ALMA using planck observations

arXiv

引用

arXiv 2021年

作者： Farren, Gerrit S. Partridge, Bruce Kneissl, Rüdiger Aiola, Simone Datta, Rahul Gralla, Megan Li, Yaqiong Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Physics and Astronomy Haverford College 370 Lancaster Ave HaverfordPA19041 United States Atacama Large Millimetre submillimetre Array ALMA Santiago Central Offices Alonso de Cordova 3107 Vitacura Casilla Santiago7630355 Chile European Southern Observatory ESO Vitacura Alonso de Cordova 3107 Vitacura Casilla Santiago19001 Chile Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy Bloomberg Center for Physics and Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Department of Astronomy Steward Observatory University of Arizona 933 N Cherry Ave TucsonAZ85721 United States Kavli Institute Department of Physics Cornell University 245 East Ave IthacaNY14850 United States

We test the accuracy of ALMA flux density calibration by comparing ALMA flux density measurements of extragalactic sources to measurements made by the Planck mission;Planck is absolutely calibrated to sub-percent precision using the dipole signal induced by the satellite's orbit around the solar system barycenter. Planck observations ended before ALMA began systematic observations, however, and many of the sources are variable, so we employ measurements by the Atacama Cosmology Telescope (ACT) to bridge the two epochs. We compare ACT observations at 93 and ∼145 GHz to Planck measurements at 100 and 143 GHz and to ALMA measurements made at 91.5 and 103.5 GHz in Band 3. For both comparisons, flux density measurements were corrected to account for the small differences in frequency using the best available spectral index for each source. We find the ALMA flux density scale (based on observations of Uranus) is consistent with Planck. All methods used to make the comparison are consistent with ALMA flux densities in Band 3 averaging 0.99 times those measured by Planck. One specific test gives ALMA/Planck = 0.996 ± 0.024. We also test the absolute calibration of both ACT at 93 and ∼145 GHz and the South Pole Telescope (SPT) at 97.43, 152.9 and 215.8 GHz, again with reference to Planck measurements at 100, 143 and 217 GHz, as well as the internal consistency of measurements of compact sources made by all three instruments. © 2021, CC BY-NC-SA.

关键词： Calibration

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：