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The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

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arXiv 2020年

作者： Choudhary, Kamal Garrity, Kevin F. Reid, Andrew C.E. DeCost, Brian Biacchi, Adam J. Hight Walker, Angela R. Trautt, Zachary Hattrick-Simpers, Jason Kusne, A. Gilad Centrone, Andrea Davydov, Albert Jiang, Jie Pachter, Ruth Cheon, Gowoon Reed, Evan Agrawal, Ankit Qian, Xiaofeng Sharma, Vinit Zhuang, Houlong Kalinin, Sergei V. Sumpter, Bobby G. Pilania, Ghanshyam Acar, Pinar Mandal, Subhasish Haule, Kristjan Vanderbilt, David Rabe, Karin Tavazza, Francesca Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research San diegoCA92037 United States Department of Chemistry and Biochemistry University of Maryland College ParkMD20742 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Materials and Manufacturing Directorate Air Force Research Laboratory Wright–Patterson Air Force Base OH45433 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Texas A&M University TX77843 United States Joint Institute for Computational Sciences University of Tennessee KnoxvilleTN37996 United States National Institute for Computational Sciences Oak Ridge National Laboratory TN37831 United States School for Engineering of Matter Transport & Energy Arizona state university AZ85287 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory TN37831 United States Materials Science and Technology Division Los Alamos National Lab Los AlamosNM87545 United States Department of Mechanical Engineering Virginia Tech BlacksburgVA24061 United States Department of Physics and Astronomy Rutgers University PiscatawayNJ08901 United States

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of ≈ 40,000 materials and ≈ 1 million calculated properties in JARVIS-DFT, ≈ 1,500 materials and ≈ 110 force-fields in JARVIS-FF, and ≈ 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://*** . © 2020, CC BY.

关键词： Density functional theory

Going viral: Stability of consensus-driven adoptive spread

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arXiv 2018年

作者： Ruf, Sebastian F. Paarporn, Keith Paré, Philip E. Center for Complex Networks Research Northeastern University Department of Electrical and Computer Engineering University of California Santa Barbara United States Division of Decision and Control Systems KTH

The spread of new products in a networked population is often modeled as an epidemic. However, in the case of "complex" contagion, these models do not capture nuanced, dynamic social reinforcement effects in adoption behavior. In this paper, we investigate a model of complex contagion which allows a coevolutionary interplay between adoption, modeled as an SIS epidemic spreading process, and social reinforcement effects, modeled as consensus opinion dynamics. Asymptotic stability analysis of the all-adopt as well as the none-adopt equilibria of the combined opinion-adoption model is provided through the use of Lyapunov arguments. In doing so, sufficient conditions are provided which determine the stability of the "flop" state, where no one adopts the product and everyone's opinion of the product is least favorable, and the "hit" state, where everyone adopts and their opinions are most favorable. These conditions are shown to extend to the bounded confidence opinion dynamic under a stronger assumption on the model parameters. To conclude, numerical simulations demonstrate behavior of the model which reflect findings from the sociology literature on adoption behavior. Copyright © 2018, The Authors. All rights reserved.

关键词： Asymptotic stability

Publisher Correction to:Highly Elastic,Bioresorbable Polymeric Materials for Stretchable,Transient Electronic Systems

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Nano-Micro Letters 2024年第8期16卷 417-417页

作者： Jeong‑Woong Shin Dong‑Je Kim Tae‑Min Jang Won Bae Han Joong Hoon Lee Gwan‑Jin Ko Seung Min Yang Kaveti Rajaram Sungkeun Han Heeseok Kang Jun Hyeon Lim Chan‑Hwi Eom Amay JBandodkar Hanul Min Suk‑Won Hwang KU‑KIST Graduate School of Converging Science and Technology Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea SK Hynix 2091Gyeongchung‑daeroBubal‑eupIcheon‑siGyeonggi‑do 17336Republic of Korea Hanwha Systems Co. Ltd.188Pangyoyeok‑roBundang‑guSeongnam‑siGyeonggi‑do 13524Republic of Korea Department of Electrical and Computer Engineering North Carolina State UniversityRaleighNC 27606USA Center for Advanced Self‑Powered Systems of Integrated Sensors and Technologies(ASSIST) North Carolina State UniversityRaleighNC 27606USA Department of Integrative Energy Engineering Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea Biomaterials Research Center Korea Institute of Science and Technology(KIST)5 Hwarang‑ro 14‑gilSeongbuk‑guSeoul 02792Republic of Korea Semiconductor R&D Center Samsung Electronics Co.Ltd.Hwaseong‑siGyeonggi‑do 18448Republic of Korea Center for Advanced Biomolecular Recognition Biomedical Research DivisionKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea

Due to typesetting mistake,Hanul Min was missed to be denoted as a corresponding author in the *** type-setter apologizes for *** original article has been *** Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were *** images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the *** material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.

关键词： Creative Elastic Highly

Learning dynamics of linear denoising autoencoders

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arXiv 2018年

作者： Pretorius, Arnu Kroon, Steve Kamper, Herman Computer Science Division Stellenbosch University South Africa CSIR/SU Centre for Artificial Intelligence Research Department of Electrical and Electronic Engineering Stellenbosch University South Africa

Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

A unified software framework for solving traffic assignment problems

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arXiv 2018年

作者： Ugirumurera, Juliette Gomes, Gabriel Porter, Emily Li, Xiaoye S. Bayen, Alexandre Computational Research Division Lawrence Berkeley Nationa Lab Institute of Transportation Studies University of California at Berkeley Department of Civil and Environmental Engineering University of California at Berkeley Department of Electrical Engineering and Computer Science University of California at Berkeley

We describe a software framework for solving user equilibrium traffic assignment problems. The design is based on the formulation of the problem as a variational inequality. The software implements these as well as several numerical methods for find equilirbria. We compare the solutions obtained under several models: static, Merchant-Nemhauser, ‘CTM with instantaneous travel time’, and ‘CTM with actual travel time’. Some important differences are demonstrated. Copyright © 2018, The Authors. All rights reserved.

关键词： Travel time

Organic Semiconductors: Fast‐Response, Highly Air‐Stable, and Water‐Resistant Organic Photodetectors Based on a Single‐Crystal Pt Complex (Adv. Mater. 2/2020)

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Advanced Materials 2020年第2期32卷

作者： Dharmaraj Periyanagounder Tzu‐Chiao Wei Ting‐You Li Chun‐Ho Lin Théo Piechota Gonçalves Hui‐Chun Fu Dung‐Sheng Tsai Jr‐Jian Ke Hung‐Wei Kuo Kuo‐Wei Huang Norman Lu Xiaosheng Fang Jr‐Hau He Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science & Technology Thuwal 23955–6900 Saudi Arabia Department of Molecular Science and Engineering National Taipei University of Technology Taipei 106 Taiwan ROC KAUST Catalyst Centre Physical Science and Engineering Division King Abdullah University of Science & Technology Thuwal 23955–6900 Saudi Arabia Institute of Organic and Polymeric Materials National Taipei University of Technology Taipei 106 Taiwan ROC Department of Materials Science Fudan University Shanghai 200433 China Department of Materials Science and Engineering City University of Hong Kong Kowloon Hong Kong SAR 999077 China

Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

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arXiv 2024年

作者： Ahumada, Tomás Anand, Shreya Coughlin, Michael W. Gupta, Vaidehi Kasliwal, Mansi M. Karambelkar, Viraj R. Stein, Robert D. Waratkar, Gaurav Swain, Vishwajeet Jegou du Laz, Theophile Anumarlapudi, Akash Andreoni, Igor Bulla, Mattia Srinivasaragavan, Gokul P. Toivonen, Andrew Wold, Avery Bellm, Eric C. Cenko, S. Bradley Kaplan, David L. Sollerman, Jesper Bhalerao, Varun Perley, Daniel Salgundi, Anirudh Suresh, Aswin Hinds, K.-Ryan Reusch, Simeon Necker, Jannis Cook, David O. Pletskova, Natalya Singer, Leo P. Banerjee, Smaranika Barna, Tyler Copperwheat, Christopher M. Healy, Brian Kiendrebeogo, R. Weizmann Kumar, Harsh Kumar, Ravi Pezzella, Marianna Sagués-Carracedo, Ana Sravan, Niharika Bloom, Joshua S. Chen, Tracy X. Graham, Matthew Helou, George Laher, Russ R. Mahabal, Ashish A. Purdum, Josiah Anupama, G.C. Barway, Sudhanshu Basu, Judhajeet Raman, Dhananjay Roychowdhury, Tamojeet Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Indian Institute of Technology West Bengal Kharagpur721302 India Department of Physics Indian Institute of Technology Bombay Powai 400 076 India Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Department of Physics and Earth Science University of Ferrara via Saragat 1 FerraraI-44122 Italy INFN Sezione di Ferrara via Saragat 1 FerraraI-44122 Italy INAF Osservatorio Astronomico d’Abruzzo via Mentore Maggini snc Teramo64100 Italy IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States The Oskar Klein Centre Department of Astronomy Albanova Stockholm University StockholmSE-106 91 Sweden Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany Department of Physics Drexel University PhiladelphiaPA19104 United States Astrophysics Research Institute IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Laboratoire de Physique et de Chimie de l’Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Artemis Observatoire de la Côte d’Azur Université Côte d’Azur Boulevard de l’Observatoire NiceF-06304 France Department of Physics Indian Institute o

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star–black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (False Alarm Rate less than 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg2 within the skymaps of the high-significance GW events, reaching a median depth of g ≈ 20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH events), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag day-1 can peak at a magnitude brighter than -17.5 mag. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

Passive phaseless SAR imaging

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Passive phaseless SAR imaging

IEEE National Conference on Radar

作者： Eric Mason Birsen Yazici Tactical Electronic Warfare Division Naval Research Laboratory Washington DC USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

Passive synthetic aperture radar (SAR) is a coherent imaging method. Thus, performance is very susceptible to phase error, which result because the transmitter is not under control of the user. These errors result from poor clock synchronization, uncertainty in transmitter location and trajectory error, to name a few. In this paper we present an incoherent imaging method for passive SAR which uses phaseless measurements. The approach is appealing over existing methods for passive radar since only a single receiver is required, and robustness to uncertainties is increased. We utilize the observation that the squared modulus of the data is equivalent to a linear mapping of a rank-one positive semi-definite operator, formed as the tensor product of scene reflectivity with itself. Therefore, we pose imaging as least-squares optimization, utilizing this structure to constrain the problem. We consider the use of both convex and non-convex constraints, for which we provide efficient and provably convergent iterative schemes. We demonstrate promising performance through numerical simulations.

关键词： Imaging Image reconstruction Radar imaging Optimization Synthetic aperture radar Receivers Transmitters

Chance-Constrained ADMM Approach for Decentralized Control of Distributed Energy Resources

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Chance-Constrained ADMM Approach for Decentralized Control o...

Power Systems Computation Conference (PSCC)

作者： Ali Hassan Yury Dvorkin Deepjyoti Deka Michael Chertkov Department of Electrical and Computer Engineering New York University New York NY USA Theory Division Los Alamos National Laboratory Los Alamos NM USA

Distribution systems are undergoing a dramatic transition from a passive circuit that routinely disseminates electric power among downstream nodes to the system with distributed energy resources. The distributed energy resources come in a variety of technologies and typically include photovoltaic (PV) arrays, thermostatically controlled loads, energy storage units. Often these resources are interfaced with the system via inverters that can adjust active and reactive power injections, thus supporting the operational performance of the system. This paper designs a control policy for such inverters using the local power flow measurements. The control actuates active and reactive power injections of the inverter-based distributed energy resources. This strategy is then incorporated into a chance-constrained, decentralized optimal power flow formulation to maintain voltage levels and power flows within their limits and to mitigate the volatility of (PV) resources. Our method is shown to improve voltage regulation compliance and reduce power losses.

关键词： Inverters Reactive power Energy resources Voltage control Power measurement Optimization Minimization