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Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic ab initio study aided by machine learning potentials

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Physical Review B 2022年第21期105卷 214302-214302页

作者： Ying Zhou Prashanth Srinivasan Fritz Körmann Blazej Grabowski Roger Smith Pooja Goddard Andrew Ian Duff School of Science Loughborough University LE11 3TU United Kingdom Institute of Materials Science University of Stuttgart Pfaffenwaldring 55 D-70569 Stuttgart Germany Materials Science and Engineering Delft University of Technology 2628 CD Delft The Netherlands Department for Computational Materials Design Max-Planck-Institut für Eisenforschung GmbH 40237 Düsseldorf Germany Scientific Computing Department STFC Daresbury Laboratory Warrington WA4 4AD United Kingdom

Multi-principal-component alloys have attracted great interest as a novel paradigm in alloy design, with often unique properties and a vast compositional space auspicious for materials discovery. High entropy alloys (HEAs) belong to this class and are being investigated for prospective nuclear applications with reported superior mechanical properties including high-temperature strength and stability compared to conventional alloys. computational materials design has the potential to play a key role in screening such alloys, yet for high-temperature properties, challenges remain in finding an appropriate balance between accuracy and computational cost. Here we develop an approach based on density-functional theory (DFT) and thermodynamic integration aided by machine learning based interatomic potential models to address this challenge. We systematically evaluate and compare the efficiency of computing the full free energy surface and thermodynamic properties up to the melting point at different stages of the thermodynamic integration scheme. Our new approach provides a ×4 speed-up with respect to comparable free energy approaches at the level of DFT, with errors on high-temperature free energy predictions less than 1 meV/atom. Calculations are performed on an equiatomic HEA, TaVCrW—a low-activation composition and therefore of potential interest for next generation fission and fusion reactors.

关键词： Thermodynamics Disordered alloys Ab initio molecular dynamics Density functional theory

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A Stochastic Galerkin Lattice Boltzmann Method for Incompressible Fluid Flows with Uncertainties

SSRN

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SSRN 2024年

作者： Zhong, Mingliang Xiao, Tianbai Krause, Mathias J. Frank, Martin Simonis, Stephan Scientific Computing Center Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing100190 China School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China Institute for Applied and Numerical Mathematics Karlsruhe Institute of Technology Karlsruhe76131 Germany Institute of Mechanical Process Engineering and Mechanics Karlsruhe Institute of Technology Karlsruhe76131 Germany Lattice Boltzmann Research Group Karlsruhe Institute of Technology Karlsruhe76131 Germany

Efficient modeling and simulation of uncertainties in computational fluid dynamics (CFD) remains a crucial challenge. In this paper, we present the first stochastic Galerkin (SG) lattice Boltzmann method (LBM) built upon the generalized polynomial chaos (gPC). The proposed method offers an efficient and accurate approach that depicts the propagation of uncertainties in stochastic incompressible flows. Formal analysis shows that the SG LBM preserves the correct Chapman-Enskog asymptotics and recovers the corresponding macroscopic fluid equations. Numerical experiments, including the Taylor-Green vortex flow, lid-driven cavity flow, and isentropic vortex convection, are presented to validate the solution algorithm. The results demonstrate that the SG LBM achieves the expected spectral convergence and the computational cost is significantly reduced compared to the sampling-based non-intrusive approaches, e.g., the routinely used Monte Carlo method. We obtain a speedup factor of 5.72 compared to Monte Carlo sampling in a randomized two-dimensional Taylor-Green vortex flow test case. By leveraging the accuracy and flexibility of LBM and the efficiency of gPC-based SG, the proposed SG LBM provides a powerful framework for uncertainty quantification in CFD practice. © 2024, The Authors. All rights reserved.

关键词： computational fluid dynamics

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Tensor network quantum virtual machine for simulating quantum circuits at exascale

arXiv

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arXiv 2021年

作者： Nguyen, Thien Lyakh, Dmitry Dumitrescu, Eugene Clark, David Larkin, Jeff McCaskey, Alexander Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States NVIDIA Corp. Santa ClaraCA United States

The numerical simulation of quantum circuits is an indispensable tool for development, verification and validation of hybrid quantum-classical algorithms on near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable tensor network processing library, ExaTN, and provides multiple configurable quantum circuit simulators enabling either exact quantum circuit simulation via the full tensor network contraction, or approximate quantum state representations via suitable tensor factorizations. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical backend we introduce an end-to-end virtual quantum development environment which can scale from laptops to future exascale platforms. We report initial benchmarks of our framework which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on the Google Sycamore superconducting architecture. Copyright © 2021, The Authors. All rights reserved.

关键词： Circuit simulation

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Claire: A distributed-memory solver for constrained large deformation diffeomorphic image registration

arXiv

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arXiv 2018年

作者： Mang, Andreas Gholami, Amir Davatzikos, Christos Biros, George Department of Mathematics University of Houston HoustonTX77204-5008 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720-1770 United States Center for Biomedical Image Computing and Analytics Department of Radiology University of Pennsylvania PhiladephiaPA19104-2643 United States Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712-1229 United States

68U10, 49J20, 35Q93, 65K10, 65F08, 76D55We introduce CLAIRE, a distributed-memory algorithm and software for solving constrained large deformation diffeomorphic image registration problems in three dimensions. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousand of distributed memory nodes on high-end clusters. Our improved, parallel implementation features parameter-, scale-, and grid-continuation schemes to speedup the computations and reduce the likelihood to get trapped in local minima. We also implement an improved preconditioner for the reduced space Hessian to speedup the convergence. We test registration performance on synthetic and real data. We demonstrate registration accuracy on 16 neuroimaging datasets. We compare the performance of our scheme against different flavors of the DEMONS algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work. Copyright © 2018, The Authors. All rights reserved.

关键词： Neuroimaging

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Composable Programming of Hybrid Workflows for Quantum Simulation

Composable Programming of Hybrid Workflows for Quantum Simul...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Thien Nguyen Lindsay Bassman Dmitry Lyakh Alexander McCaskey Vicente Leyton-Ortega Raphael Pooser Wael Elwasif Travis S. Humble Wibe A. de Jong Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA Oak Ridge National Laboratory Quantum Computing Institute Oak Ridge TN USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley California USA National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Computer Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781665439107;9781665429870

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM, running some prototypical quantum simulations.

关键词： Program processors Heuristic algorithms Object oriented modeling Stationary state Fault tolerant systems Programming Libraries

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A Secure Authentication Mechanism for Wireless Sensor Networks

A Secure Authentication Mechanism for Wireless Sensor Networ...

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International Conference on Computer, Information and Telecommunication Systems (CITS)

作者： Rifaqat Ali Preeti Chandrakar Mohammad S. Obaidat Kuei-Fang Hsiao Arup Kumar Pal SK Hafizul Islam Department of Mathematics and Scientific Computing National Institute of Technology Hamirpur Himachal Pradesh India Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh India College of Computing and Informatics University of Sharjah Sharjah UAE KAIST University of Jordan Amman Jordan University of Science and Technology Beijing China Amity University Noida UP India Department of Information Systems University of Sharjah Sharjah UAE Department of Information Management Ming Chuan University Taiwan Department of Computer Science and Engineering Indian Institute of Technology (ISM) Dhanbad Jharkhand India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal India

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

In Wireless Sensor Networks (WSNs), the sensor nodes are often used to collect the data and send it to the gateway node. This real data are the most important and sensitive. If someone gets this data illegally, then there is no privacy. Thus, user authentication is most precious for securing this data over an insecure channel. To keep this important matter in mind, we have developed a secure authentication mechanism for WSNs. The proposed method is proved through BAN logic, which guarantees mutual authentication and session key agreement securely. The informal security analysis is also executed for the proposed mechanism to show that it resists various wicked attacks. Apart from this, the performance evaluation is also carried out along with other existing schemes in regards of communication and computation costs.

关键词： Wireless sensor networks Authentication Logic gates Password Coplanar waveguides computational modeling Security

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Modeling the thickness dependence of the magnetic phase transition temperature in thin FeRh films

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Physical Review B 2017年第6期95卷 064415-064415页

作者： Thomas Andrew Ostler Craig Barton Thomas Thomson Gino Hrkac Université de Liège Physique des Matériaux et Nanostructures Liège B-4000 Sart Tilman Belgium College of Engineering Mathematics and Physical Sciences The University of Exeter Exeter EX4 4SB United Kingdom School of Computer Science The University of Manchester Oxford Road Manchester M13 9PL United Kingdom Institute for Analysis and Scientific Computing TU Wien Vienna Austria

FeRh and its first-order phase transition can open new routes for magnetic hybrid materials and devices under the assumption that it can be exploited in ultra-thin-film structures. Motivated by experimental measurements showing an unexpected increase in the phase transition temperature with decreasing thickness of FeRh on top of MgO, we develop a computational model to investigate strain effects of FeRh in such magnetic structures. Our theoretical results show that the presence of the MgO interface results in a strain that changes the magnetic configuration which drives the anomalous behavior.

关键词： Magnetic interactions Magnetic phase transitions Magnetism Magnetic multilayers

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Improving the Noise Estimation of Latent Neural Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Heck, Linus Gelbrecht, M. Schaub, M. Boers, N. Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Earth System Modelling School of Engineering and Design TU Munich Germany Computational Network Science Dept of Computer Science RWTH Aachen University Germany Department of Computer Science RWTH Aachen University Germany Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Department of Mathematics and Statistics University of Exeter Exeter United Kingdom

Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they systematically underestimate the noise level inherent in such data, limiting their ability to capture stochastic dynamics accurately. We investigate this underestimation in detail and propose a straightforward solution: by including an explicit additional noise regularization in the loss function, we are able to learn a model that accurately captures the diffusion component of the data. We demonstrate our results on a conceptual model system that highlights the improved latent neural SDE’s capability to model stochastic bistable dynamics. © 2024, CC BY-NC-ND.

关键词： Stochastic models

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Quantum simulation of the Fokker-Planck equation via Schrödingerization

arXiv

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arXiv 2024年

作者： Jin, Shi Liu, Nana Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

This paper studies a quantum simulation technique for solving the Fokker-Planck equation. Traditional semi-discretization methods often fail to preserve the underlying Hamiltonian dynamics and may even modify the Hamiltonian structure, particularly when incorporating boundary conditions. We address this challenge by employing the Schrödingerization method - it converts any linear partial and ordinary differential equation with non-Hermitian dynamics into systems of Schrödinger-type equations. It does so via the so-called warped phase transformation that maps the equation into one higher dimension. We explore the application in two distinct forms of the Fokker-Planck equation. For the conservation form, we show that the semi-discretization-based Schrödingerization is preferable, especially when dealing with non-periodic boundary conditions. Additionally, we analyze the Schrödingerization approach for unstable systems that possess positive eigenvalues in the real part of the coefficient matrix or differential operator. Our analysis reveals that the direct use of Schrödingerization has the same effect as a stabilization procedure. For the heat equation form, we propose a quantum simulation procedure based on the time-splitting technique, and give explicitly its corresponding quantum circuit. We discuss the relationship between operator splitting in the Schrödingerization method and its application directly to the original problem, illustrating how the Schrödingerization method accurately reproduces the time-splitting solutions at each step. Furthermore, we explore finite difference discretizations of the heat equation form using shift operators. Utilizing Fourier bases, we diagonalize the shift operators, enabling efficient simulation in the frequency space. Providing additional guidance on implementing the diagonal unitary operators, we conduct a comparative analysis between diagonalizations in the Bell and the Fourier bases, and show that the former generally exhibit

关键词： Hamiltonians

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Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance

arXiv

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arXiv 2023年

作者： Lee, Mingyu Shin, Myeongjin Lee, Junseo Jeong, Kabgyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of QDQ Corp. Seoul06999 Korea Republic of School of Computing KAIST Daejeon34141 Korea Republic of School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Quantum Computing R&D Norma Inc. Seoul04799 Korea Republic of Research Institute of Mathematics Seoul National University Seoul08826 Korea Republic of School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of

One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine learning across various domains. Specifically, generative adversarial networks have been recognized for their potential utility in diverse fields such as image generation, finance, and probability distribution modeling. However, these networks necessitate solutions for inherent challenges like mode collapse. In this study, we capitalize on the concept that the estimation of mutual information between high-dimensional continuous random variables can be achieved through gradient descent using neural networks. We introduce a novel approach named InfoQGAN, which employs the Mutual Information Neural Estimator (MINE) within the framework of quantum generative adversarial networks to tackle the mode collapse issue. Furthermore, we elaborate on how this approach can be applied to a financial scenario, specifically addressing the problem of generating portfolio return distributions through dynamic asset allocation. This illustrates the potential practical applicability of InfoQGAN in real-world contexts. © 2023, CC BY.

关键词： Generative adversarial networks

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