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Generation and structural characterization of Debye random media

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arXiv 2020年

作者： Ma, Zheng Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

In their seminal paper on scattering by an inhomogeneous solid, Debye and coworkers proposed a simple exponentially decaying function for the two-point correlation function of an idealized class of two-phase random media. Such Debye random media, which have been shown to be realizable, are singularly distinct from all other models of two-phase media in that they are entirely defined by their one- and two-point correlation functions. To our knowledge, there has been no determination of other microstructural descriptors of Debye random media. In this paper, we generate Debye random media in two dimensions using an accelerated Yeong-Torquato construction algorithm. We then ascertain microstructural descriptors of the constructed media, including their surface correlation functions, pore-size distributions, lineal-path function, and chord-length probability density function. Accurate semi-analytic and empirical formulas for these descriptors are devised. We compare our results for Debye random media to those of other popular models (overlapping disks and equilibrium hard disks), and find that the former model possesses a wider spectrum of hole sizes, including a substantial fraction of large holes. Our algorithm can be applied to generate other models defined by their two-point correlation functions, and their other microstructural descriptors can be determined and analyzed by the procedures laid out here. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

A growth model for water distribution networks with loops

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arXiv 2021年

作者： Sugishita, Kashin Abdel-Mottaleb, Noha Zhang, Qiong Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs which generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modeled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency, and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modeled pipe diameters. The difference between the empirical and modeled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

End-To-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

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Physical Review Applied 2020年第6期14卷 064020-064020页

作者： Re-Bing Wu Xi Cao Pinchen Xie Yu-xi Liu Department of Automation Tsinghua University Beijing 100084 China Beijing National Research Center for Information Science and Technology Beijing 100084 China Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Institute of Microelectronics Tsinghua University Beijing 100084 China

To achieve quantum machine learning on imperfect noisy intermediate-scale quantum (NISQ) processors, the entire physical implementation should include as few as possible hand-designed modules with only a few ad hoc parameters to be determined. This work presents such a hardware-friendly end-to-end quantum-machine-learning scheme that can be implemented with imperfect NISQ processors. The proposal transforms the machine-learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables. Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables. Comparing with the gate-based parameterized quantum circuits, the proposed end-to-end quantum-learning model is easy to implement as there are only a few ad hoc parameters to be determined. Numerical simulations on the benchmarking MNIST (Mixed National Institute of Standards and technology) dataset of handwritten digits demonstrate that the model can achieve high performance using only 3–5 qubits without downsizing the dataset, which shows great potential for accomplishing large-scale real-world learning tasks on NISQ processors.

关键词： Machine learning Noise Quantum control Quantum information architectures & platforms Artificial neural networks

Biases in inverse Ising estimates of near-critical behavior

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Physical Review E 2023年第1期108卷 014109-014109页

作者： Maximilian B. Kloucek Thomas Machon Shogo Kajimura C. Patrick Royall Naoki Masuda Francesco Turci School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Bristol Centre for Functional Nanomaterials HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto 606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL 75005 Paris France Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

关键词： Critical phenomena Cortical networks Spin glasses Functional magnetic resonance imaging Ising model

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

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arXiv 2023年

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

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

关键词： Molecular dynamics

Hyperuniformity order metric of Barlow packings

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Physical Review E 2019年第2期99卷 022111-022111页

作者： T. M. Middlemas F. H. Stillinger S. Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University New Jersey 08544 USA

The concept of hyperuniformity has been a useful tool in the study of density fluctuations at large length scales in systems ranging across the natural and mathematical sciences. One can rank a large class of hyperuniform systems by their ability to suppress long-range density fluctuations through the use of a hyperuniformity order metric Λ¯. We apply this order metric to the Barlow packings, which are the infinitely degenerate densest packings of identical rigid spheres that are distinguished by their stacking geometries and include the commonly known fcc lattice and hcp crystal. The “stealthy stacking” theorem implies that these packings are all stealthy hyperuniform, a strong type of hyperuniformity, which involves the suppression of scattering up to a wave vector K. We describe the geometry of three classes of Barlow packings, two disordered classes and small-period packings. In addition, we compute a lower bound on K for all Barlow packings. We compute Λ¯ for the aforementioned three classes of Barlow packings and find that, to a very good approximation, it is linear in the fraction of fcc-like clusters, taking values between those of least-ordered hcp and most-ordered fcc. This implies that the value of Λ¯ of all Barlow packings is primarily controlled by the local cluster geometry. These results highlight the special nature of anisotropic stacking disorder, which provides impetus for future research on the development of anisotropic order metrics and hyperuniformity properties.

关键词： Classical statistical mechanics

Low-energy astrophysics with KamLAND 37

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Low-energy astrophysics with KamLAND

37th International Cosmic Ray Conference, ICRC 2021

作者： Kawada, Nanami Obara, Shuhei Ishidoshiro, Koji Grant, C. O’Donnell, T. Dell’Oro, S. Abe, S. Asami, S. Gando, A. Gando, Y. Gima, T. Goto, A. Hachiya, T. Hata, K. Hayashida, S. Hosokawa, K. Ichimura, K. Ieki, S. Ikeda, H. Inoue, K. Kamei, Y. Kishimoto, Y. Kinoshita, T. Koga, M. Maemura, N. Mitsui, T. Miyake, H. Nakamura, K. Nakamura, K. Nakamura, R. Ozaki, H. Sakai, T. Sambonsugi, H. Shimizu, I. Shirai, J. Shiraishi, K. Suzuki, A. Suzuki, Y. Takeuchi, A. Tamae, K. Ueshima, K. Wada, Y. Watanabe, H. Yoshida, Y. Ichikawa, A.K. Kozlov, A. Chernyak, D. Takemoto, Y. Yoshida, S. Umehara, S. Fushimi, K. Nakamura, K.Z. Yoshida, M. Berger, B.E. Fujikawa, B.K. Learned, J.G. Maricic, J. Axani, S.N. Winslow, L.A. Fu, Z. Ouellet, J. Efremenko, Y. Karwowski, H.J. Markoff, D.M. Tornow, W. Li, A. Detwiler, J.A. Enomoto, S. Decowski, M.P. Research Center for Neutrino Science Tohoku University Sendai980-8578 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan Institute for the Physics and Mathematics of the Universe The University of Tokyo Kashiwa277-8568 Japan Graduate Program on Physics for the Universe Tohoku University Sendai980-8578 Japan Department of Physics Tohoku University Sendai980-8578 Japan Graduate School of Science Osaka University Osaka Toyonaka560-0043 Japan Osaka University Osaka Ibaraki567-0047 Japan Graduate School of Advanced Technology and Science Tokushima University Tokushima770-8506 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics and Astronomy University of Hawaii at Manoa HonoluluHI96822 United States Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Triangle Universities Nuclear Laboratory DurhamNC27708 United States The University of North Carolina at Chapel Hill Chapel HillNC27599 United States North Carolina Central University DurhamNC27701 United States Physics Department Duke University DurhamNC27705 United States Center for Experimental Nuclear Physics and Astrophysics University of Washington SeattleWA98195 United States Nikhef The University of Amsterdam Science Park Amsterdam Netherlands Boston University BostonMA02215 United States Center for Neutrino Physics Virginia Polytechnic Institute State University BlacksburgVA24061 United States

We present two results of a search for MeV-scale neutrino and anti-neutrino events correlated with gravitational wave events/candidates and large solar flares with KamLAND. The KamLAND detector is a large-volume neutrino detector using liquid scintillator, which is located at 1 km underground under the top of Mt. Ikenoyama in Kamioka, Japan. KamLAND has multiple reaction channels to detect neutrinos. Electron antineutrino can be detected via inverse-beta decay with 1.8 MeV neutrino energy threshold. All flavors of neutrinos can be detected via neutrino-electron scattering without neutrino energy threshold. KamLAND has continued the neutrino observation since 2002 March. We use the data set of 60 gravitational waves provided by the LIGO/Virgo collaboration during their second and third observing runs and search for coincident electron antineutrino events in KamLAND. We find no significant coincident signals within a ±500 s timing window from each gravitational wave and present 90% C.L. upper limits on the electron antineutrino fluence between 108–1013 cm−2 for neutrino energies of 1.8–111 MeV. For a solar-flare neutrino search at KamLAND, we determine the timing window using the solar X-ray data set provided by the GOES satellite series from 2002 to 2019 and search for the excess of coincident event rate on the all-flavor neutrinos. We find no significant event rate excess in the flare time windows and get 90% C.L. upper limits on the fluence of neutrinos of all flavors (electron anti-neutrinos) between 1010–1013 cm−2 (108–1013 cm−2) for neutrino energies in the energy range of 0.4–35 MeV. © Copyright owned by the author(s).

关键词： Gravity waves

The prediction of molecule atomization energy using neural network and extreme gradient boosting

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Journal of Physics: Conference Series 2021年第1期2072卷

作者： M Sumanto M A Martoprawiro A L Ivansyah Master Program in Computational Science Faculty of Mathematics and Natural Sciences Bandung Institute of Technology Bandung 40132 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Bandung Institute of Technology Bandung 40132 Indonesia Department of Computational Science Faculty of Mathematics and Natural Sciences Bandung Institute of Technology Bandung 40132 Indonesia

Machine Learning is an artificial intelligence system, where the system has the ability to learn automatically from experience without being explicitly programmed. The learning process from Machine Learning starts from observing the data and then looking at the pattern of the data. The main purpose of this process is to make computers learn automatically. In this study, we will use Machine Learning to predict molecular atomization energy. From various methods in Machine Learning, we use two methods namely Neural Network and Extreme Gradient Boosting. Both methods have several parameters that must be adjusted so that the predicted value of the atomization energy of the molecule has the lowest possible error. We are trying to find the right parameter values for both methods. For the neural network method, it is quite difficult to find the right parameter value because it takes a long time to train the model of the neural network to find out whether the model is good or bad, while for the Extreme Gradient Boosting method the time needed to train the model is shorter, so it is quite easy to find the right parameter values for the model. This study also looked at the effects of the modification on the dataset with the output transformation of normalization and standardization then removing molecules containing Br atoms and changing the entry in the Coulomb matrix to 0 if the distance between atoms in the molecule exceeds 2 angstrom.

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Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction

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arXiv 2020年

作者： Zhang, Jiachen Crippa, Paola Genton, Marc G. Castruccio, Stefano Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Department of Civid and Environmental Engineering and Geoscience University of Notre Dame United States Statistics Program King Abdullah University of Science and Technology Saudi Arabia

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience, and hence profitability, of future wind farms to a changing climate, given that current wind turbines have lifespans of up to thirty years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a cluster-wise minimization of the Kullback-Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country. Copyright © 2020, The Authors. All rights reserved.

关键词： Wind power