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arXiv 2023年

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

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

关键词： Multi-task learning

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Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network

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Physical Review Fluids 2019年第10期4卷 104605-104605页

作者： Chenyue Xie Ke Li Chao Ma Jianchun Wang Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences Beijing 100190 People's Republic of China The Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local stencil geometry with Galilean invariance. The input features include the first-order and second-order derivatives of filtered velocity and temperature, filtered density, and its first-order derivative. It is shown that the proposed ANN-F7 model shows an advantage over the gradient model in the a priori test. Specifically, the ANN-F7 model gives larger correlation coefficients and smaller relative errors than the gradient model. In an a posteriori analysis, the ANN-F7 model performs better than the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the statistical properties of flow fields at the Taylor microscale Reynolds number Reλ ranging from 180 to 250. The DSM and DMM models lead to the typical tilted spectral distribution of velocity, where low wave numbers are too energy rich, while those near the cutoff are damped too strongly. In contrast, it is shown that the velocity spectrum predicted by the ANN-F7 model almost overlaps with the filtered direct numerical simulation data. Besides, the ANN-F7 model reconstructs the probability density functions of SGS force and divergence of SGS heat flux much better than the DSM and DMM models. An artificial neural network with reasonable physical input features can deepen our understanding of turbulence modeling.

关键词： Compressible flows Turbulence Artificial neural networks

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Quality of internal representation shapes learning performance in feedback neural networks

arXiv

引用

arXiv 2020年

作者： Susman, Lee Mastrogiuseppe, Francesca Brenner, Naama Barak, Omri Interdisciplinary Program in Applied Mathematics Technion Israel Institute of Technology Haifa32000 Israel Network Biology Research Laboratories Technion Israel Institute of Technology Haifa32000 Israel Gatsby Computational Neuroscience Unit University College London London United Kingdom Dept. of Chemical Engineering Technion Israel Institute of Technology Haifa32000 Israel Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa32000 Israel

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study non-linear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, de-synchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behaviour of performance in non-linear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained non-linear networks to explain behaviours which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task. © 2020, CC BY.

关键词： Quality control

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Analysis of Lead (Pb) and Zinc (Zn) Content in Sediments and Faunus ater at Bale Lhoknga Aceh Besar District 1

Analysis of Lead (Pb) and Zinc (Zn) Content in Sediments and...

引用

1st International Conference on Education, Science and Technology, ICON-EST 2018

作者： Agustina, Rahmi Sarong, M. Ali Yulianda, Fredinan Suhendrayatna Rahmadi Lelifajri Graduate School of Mathematics and Applied Sciences Syiah Kuala University Banda Aceh23111 Indonesia Departement of Biology Faculty of Teacher Training and Education Jabal Ghafur University Sigli24171 Indonesia Departement of Biology Faculty of Teacher Training and Education Syiah Kuala University Banda Aceh23111 Indonesia Departement of Water Resources Management Faculty of Fisheries and Marine Sciences Bogor Agricultural University Bogor16680 Indonesia Department of Chemical Engineering Faculty of Engineering Syiah Kuala University Banda Aceh23111 Indonesia Department of Chemistry Faculty of Mathematics and Science Post Graduate Program Syiah Kuala University Banda Aceh23111 Indonesia Department of Chemistry Faculty of Mathematics and Sci. Post Graduate Program Syiah Kuala Univ. Banda Aceh23111 Indonesia

Faunus ater is a favoritemollusca that is often found on the Bale River. Local people use these snails as food for consumption along with their main food. Bale River itself is located adjacent to the cement factory, used water washing machines distributed to the river is feared to support the presence of lead (Pb) and zinc (Zn) metals in the river. This study aims to analyze the relationship of Pb and Zn content in sediments to the content of Pb and Zn in Faunus ater. Data collection was carried out in Februari until April 2018. The relationship between Pb and Zn content in sediments with Pb and Zn content in the body of Faunus ater was analyzed by correlation analysis. The results of the data analysis showed that the metal content of Pb and Zn in sediment and Faunus aterwere found to vary. The highest Pb content in sediment was found at station 2 in March, amounting to 32.87 mg / kg. while the highest Zn content was found at station 1 in March amounting to 84 mg / kg. The relationship of heavy metals Pb and Zn in sediments to the content of Pb and Zn in the body of Faunus ater showed a negative correlation. The conclusion of this study is that there is no correlation between the content of Pb and Zn heavy metals in sediments on the content of lead Pb and Zn in the body of Faunus ater at the Bale Lhoknga River in Aceh Besar District. © Published under licence by IOP Publishing Ltd.

关键词： Rivers

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Meshless Hermite-HDMR finite difference method for high-dimensional Dirichlet problems

arXiv

引用

arXiv 2019年

作者： Luo, Xiaopeng Xu, Xin Rabitz, Herschel Department of Chemistry Princeton University PrincetonNJ08544 United States School of Managment and Engineering Nanjing University Nanjing210008 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

In this paper, a meshless Hermite-HDMR finite difference method is proposed to solve high-dimensional Dirichlet problems. The approach is based on the local Hermite-HDMR expansion with an additional smoothing technique. First, we introduce the HDMR decomposition combined with the multiple Hermite series to construct a class of Hermite-HDMR approximations, and the relevant error estimate is theoretically built in a class of Hermite spaces. It can not only provide high order convergence but also retain good scaling with increasing dimensions. Then the Hermite-HDMR based finite difference method is particularly proposed for solving high-dimensional Dirichlet problems. By applying a smoothing process to the Hermite-HDMR approximations, numerical stability can be guaranteed even with a small number of nodes. Numerical experiments in dimensions up to 30 show that resulting approximations are of very high quality. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite difference method

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Numerical meshless solution of high-dimensional sine-gordon equations via fourier HDMR-HC approximation

arXiv

引用

arXiv 2019年

作者： Xu, Xin Luo, Xiaopeng Rabitz, Herschel Department of Chemistry Princeton University PrincetonNJ08544 United States School of Managment and Engineering Nanjing University Nanjing210008 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

In this paper, an implicit time stepping meshless scheme is proposed to find the numerical solution of high-dimensional sine-Gordon equations (SGEs) by combining the high dimensional model representation (HDMR) and the Fourier hyperbolic cross (HC) approximation. To ensure the sparseness of the relevant coefficient matrices of the implicit time stepping scheme, the whole domain is first divided into a set of subdomains, and the relevant derivatives in high-dimension can be separately approximated by the Fourier HDMR-HC approximation in each subdomain. The proposed method allows for stable large time-steps and a relatively small number of nodes with satisfactory accuracy. The numerical examples show that the proposed method is very attractive for simulating the high-dimensional SGEs. Copyright © 2019, The Authors. All rights reserved.

关键词： Numerical methods

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Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

arXiv

引用

arXiv 2020年

作者： Law, Jeffrey N. Akers, Kyle Tasnina, Nure Della Santina, Catherine M. Deutsch, Shay Kshirsagar, Meghana Klein-Seetharaman, Judith Crovella, Mark Rajagopalan, Padmavathy Kasif, Simon Murali, T.M. Interdisciplinary Ph.D. Program in Genetics Bioinformatics and Computational Biology BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Mathematics University of California Los AngelesCA United States AI for Good Lab Microsoft RedmondWA United States Department of Chemistry Colorado School of Mines GoldenCO United States Department of Computer Science Boston University BostonMA United States Department of Chemical Engineering Virginia Tech BlacksburgVA United States

Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results: We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions: We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses. © 2020, CC BY-NC-SA.

关键词： SARS

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Variational Benchmarks for Quantum Many-Body Problems

arXiv

引用

arXiv 2023年

作者： Wu, Dian Rossi, Riccardo Vicentini, Filippo Astrakhantsev, Nikita Becca, Federico Cao, Xiaodong Carrasquilla, Juan Ferrari, Francesco Georges, Antoine Hibat-Allah, Mohamed Imada, Masatoshi Läuchli, Andreas M. Mazzola, Guglielmo Mezzacapo, Antonio Millis, Andrew Moreno, Javier Robledo Neupert, Titus Nomura, Yusuke Nys, Jannes Parcollet, Olivier Pohle, Rico Romero, Imelda Schmid, Michael Silvester, J. Maxwell Sorella, Sandro Tocchio, Luca F. Wang, Lei White, Steven R. Wietek, Alexander Yang, Qi Yang, Yiqi Zhang, Shiwei Carleo, Giuseppe LausanneCH-1015 Switzerland LausanneCH-1015 Switzerland ParisF-75005 France CPHT CNRS École Polytechnique Institut Polytechnique de Paris PalaiseauF-91128 France Collège de France Université PSL ParisF-75005 France Department of Physics University of Zurich ZurichCH-8057 Switzerland Dipartimento di Fisica Università di Trieste TriesteI-34151 Italy Center for Computational Quantum Physics Flatiron Institute New YorkNY10010 United States Vector Institute MaRS Centre TorontoONM5G 1M1 Canada Institute for Theoretical Physics ETH Zürich ZurichCH-8093 Switzerland Institut für Theoretische Physik Goethe-Universität Frankfurt am Main60438 Germany Department of Quantum Matter Physics Université de Genève GenevaCH-1211 Switzerland Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Toyota Physical and Chemical Research Institute Aichi Nagakute480-1192 Japan Waseda Research Institute for Science and Engineering Waseda University Shinjuku-ku Tokyo169-8555 Japan Physics Division Sophia University Chiyoda-ku Tokyo102-8554 Japan Department of Applied Physics University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Theoretical and Computational Physics Paul Scherrer Institute VilligenCH-5232 Switzerland Institute for Computational Science University of Zurich ZurichCH-8057 Switzerland IBM Quantum T. J. Watson Research Center Yorktown HeightsNY10598 United States Department of Physics Columbia University New YorkNY10027 United States Center for Quantum Phenomena Department of Physics New York University New YorkNY10003 United States Department of Applied Physics and Physico-Informatics Keio University Kohoku-ku Yokohama223-8522 Japan Institute for Materials Research Tohoku University Aoba-ku Sendai980-8577 Japan Université Pa

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy, and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods toward a quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

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Isotope effects in liquid water via deep potential molecular dynamics

arXiv

引用

arXiv 2019年

作者： Ko, Hsin-Yu Zhang, Linfeng Santra, Biswajit Wang, Han Weinan, E. DiStasio, Robert A. Cara, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry and Chemical Biology Cornell University IthacaNY14853 United States Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model - a neural-network representation of the ab initio PES - in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2O and D2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Heterogeneous inputs to central pattern generators can shape insect gaits.

arXiv

引用

arXiv 2018年

作者： Aminzare, Zahra Holmes, Philip Program in Applied and Computational Mathematics Princeton University NJ United States Program in Applied and Computational Mathematics Department of Mechanical and Aerospace Engineering Princeton Neuroscience Institute Princeton University NJ United States

In our previous work [1], we studied an interconnected bursting neuron model for insect locomotion, and its corresponding phase oscillator model, which at high speed can generate stable tripod gaits with three legs off the ground simultaneously in swing, and at low speed can generate stable tetrapod gaits with two legs off the ground simultaneously in swing. However, at low speed several other stable locomotion patterns, that are not typically observed as insect gaits, may coexist. In the present paper, by adding heterogeneous external input to each oscillator, we modify the bursting neuron model so that its corresponding phase oscillator model produces only one stable gait at each speed, specifically: a unique stable tetrapod gait at low speed, a unique stable tripod gait at high speed, and a unique branch of stable transition gaits connecting them. This suggests that control signals originating in the brain and central nervous system can modify gait patterns.34C15, 34C60, 37G10, 92B20, 92C20 Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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