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Corrigendum to "Synergetic effect of bismuth vanadate over copolymerized carbon nitride composites for highly efficient photocatalytic H2 and O2 generation" [J. Colloid Interface Sci. 627 (2022) 621-629]

引用

Journal of colloid and interface science 2022年第PT A期628卷 366-367页

作者： Asif Hayat Muhammad Sohail T A Taha Sunil Kumar Baburao Mane Abdullah G Al-Sehemi Ahmed A Al-Ghamdi W I Nawawi Arkom Palamanit Mohammed A Amin Ahmed M Fallatah Zeeshan Ajmal Hamid Ali Wasim Ullah Khan Muhammad Wajid Shah Javid Khan S Wageh State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry Fuzhou University Fuzhou 350116 China. Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Huzhou 313001 China. Physics Department College of Science Jouf University P.O. Box: 2014 Sakaka Saudi Arabia Physics and Engineering Mathematics Department Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt. Department of Chemistry Khaja Bandanawaz University Kalaburagi Karnataka 585104 India. Research Center for Advanced Materials Science (RCAMS) King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia Department of Chemistry College of Science King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia. Department of Physics Faculty of Science King Abdulaziz University Jeddah 21589 Saudi Arabia. Faculty of Applied Sciences Universiti Teknologi MARA Cawangan Perlis Arau Perlis 02600 Malaysia. Energy Technology Program Department of Specialized Engineering Faculty of Engineering Prince of Songkla University 15 Karnjanavanich Rd. Hat Yai Songkhla 90110 Thailand. Department of Chemistry College of Science Taif University P.O. Box 11099 Taif 21944 Saudi Arabia. School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xian 710072 China. Multiscale Computational Materials Facility Key Laboratory of Eco-Materials Advanced Technology College of Materials Science and Engineering Fuzhou University Fuzhou 350100 China. State Key Laboratory of Optoelectronic Materials and Technologies School of Materials Science and Engineering Sun Yat-sen University Guangzhou 510275 China. Electronic address: wasimullah89@***. School Of Chemistry Sun Yat-sen University Guangzhou 510275 China. Electronic address: m.wajidshah@***. College of Materials Science and Engineering Hunan University Changsha Hunan 410082 China. Electronic address: javidchemist@yahoo.com.

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DPA-2: a large atomic model as a multi-task learner

arXiv

引用

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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Frequency-Resolved Optical Gating Recovery via Smoothing Gradient

arXiv

引用

arXiv 2019年

作者： Pinilla, Samuel Bendory, Tamir Eldar, Yonina C. Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander BucaramangaSantander680002 Colombia Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Weizmann Institute of Science Rehovot7610001 Israel Department of Computer Science Universidad Industrial de Santander BucaramangaSantander680002 Colombia

Frequency-resolved optical gating (FROG) is a popular technique for complete characterization of ultrashort laser pulses. The acquired data in FROG, called FROG trace, is the Fourier magnitude of the product of the unknown pulse with a time-shifted version of itself, for several different shifts. To estimate the pulse from the FROG trace, we propose an algorithm that minimizes a smoothed non-convex least-squares objective function. The method consists of two steps. First, we approximate the pulse by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of block stochastic gradient iterations. The algorithm is theoretically simple, numerically scalable, and easy-to-implement. Empirically, our approach outperforms the state-of-the-art when the FROG trace is incomplete, that is, when only few shifts are recorded. Simulations also suggest that the proposed algorithm exhibits similar computational cost compared to a state-of-the-art technique for both complete and incomplete data. In addition, we prove that in the vicinity of the true solution, the algorithm converges to a critical point. A Matlab implementation is publicly available at https://***/samuelpinilla/FROG. Copyright © 2019, The Authors. All rights reserved.

关键词： Ultrashort pulses

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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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The Nystrom Extension for Signals Defined on a Graph

The Nystrom Extension for Signals Defined on a Graph

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ayelet Heimowitz Yonina C. Eldar The Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Technion Haifa Israel

ISBN: (纸本)9781538646595

In this paper we introduce a computationally efficient solution to the problem of graph signal interpolation. Our solution is derived using the Nyström extension and is due to the properties of the Markov matrix which we use as our graph shift operator, inspired by diffusion maps. We focus on graph signals that are smooth over the graph. This assumption cements the relationship between the graph and the graph signal. We experimentally verify our suggested framework on the MNIST data set of handwritten digits.

关键词： Markov processes Signal processing Interpolation Laplace equations Symmetric matrices Image edge detection Eigenvalues and eigenfunctions

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AN INFEASIBLE-START FRAMEWORK FOR CONVEX QUADRATIC OPTIMIZATION, WITH APPLICATION TO CONSTRAINT-REDUCED INTERIOR-POINT METHODS ∗

arXiv

引用

arXiv 2019年

作者： Paul Laiu, M. Tits, André L. Computational and Applied Mathematics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequalityconstrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) `1-least relaxed feasible problem when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more constraints than variables ("imbalanced"). Numerical comparison with popular codes (SDPT3, SeDuMi, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Support vector machines

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Unsupervised co-learning on g-manifolds across irreducible representations

arXiv

引用

arXiv 2019年

作者： Fan, Yifeng Gaoy, Tingran Zhaoz, Zhizhen Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61820 United States Committee on Computational and Applied Mathematics Department of Statistics University of Chicago ChicagoIL60637 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61820 United States

We introduce a novel co-learning paradigm for manifolds naturally equipped with a group action, motivated by recent developments on learning a manifold from attached fibre bundle structures. We utilize a representation theoretic mechanism that canonically associates multiple independent vector bundles over a common base manifold, which provides multiple views for the geometry of the underlying manifold. The consistency across these fibre bundles provide a common base for performing unsupervised manifold co-learning through the redundancy created artificially across irreducible representations of the transformation group. We demonstrate the efficacy of the proposed algorithmic paradigm through drastically improved robust nearest neighbor search and community detection on rotation-invariant cryo-electron microscopy image analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Low-Frequency Nonresonant Rectification in Spin Diodes

引用

Physical Review applied 2020年第2期14卷 024043-024043页

作者： R. Tomasello B. Fang P. Artemchuk M. Carpentieri L. Fasano A. Giordano O.V. Prokopenko Z.M. Zeng G. Finocchio Institute of Applied and Computational Mathematics FORTH GR-70013 Heraklion-Crete Greece Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS Suzhou Jiangsu 215123 People’s Republic of China Faculty of Radiophysics Electronics and Computer Systems Taras Shevchenko National University of Kyiv Kyiv 01601 Ukraine Department of Physics Oakland University Rochester Michigan 48309 USA Department of Electrical and Information Engineering Politecnico of Bari via Orabona 4 70125 Bari Italy Italian Space Agency (ASI) Rome Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina I-98166 Messina Italy

Spin diodes are usually resonant in nature (GHz frequency) and tunable by magnetic field and bias current with performances, in terms of sensitivity and minimum detectable power, overcoming the semiconductor counterpart, i.e., Schottky diodes. Recently, spin diodes characterized by a low-frequency detection (MHz frequency) have been proposed. Here, we show a strategy to design low-frequency detectors based on magnetic tunnel junctions with the interfacial perpendicular anisotropy of the same order of the demagnetizing field out-of-plane component. Micromagnetic calculations show that, to reach this detection regime, a threshold input power has to be overcome and the phase shift between the oscillation magnetoresistive signal and the input radiofrequency current plays a key role in determining the value of the rectification voltage.

关键词： Spintronics Magnetic tunnel junctions Ferromagnetic resonance Micromagnetic modeling

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Leveraging randomized compiling for the QITE algorithm

arXiv

引用

arXiv 2021年

作者： Ville, Jean-Loup Morvan, Alexis Hashim, Akel Naik, Ravi K. Lu, Marie Mitchell, Bradley Kreikebaum, John-Mark O’Brien, Kevin P. Wallman, Joel J. Hincks, Ian Emerson, Joseph Smith, Ethan Younis, Ed Iancu, Costin Santiago, David I. Siddiqi, Irfan Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Inst. for Quantum Computing Dept. of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices. © 2021, CC BY.

关键词： Benchmarking

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