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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

F*** workflows: when parts of FAIR are missing

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arXiv 2022年

作者： Wilkinson, Sean R. Eisenhauer, Greg Kapadia, Anuj J. Knight, Kathryn Logan, Jeremy Widener, Patrick Wolf, Matthew Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States College of Computing Georgia Institute of Technology AtlantaGA United States Computational Sciences and Engineering Oak Ridge National Laboratory Oak RidgeTN United States Computer Science and Mathematics Oak Ridge National Laboratory Oak RidgeTN United States

The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows. © 2022, CC BY.

关键词： Computer software reusability

Benchmarking noisy intermediate scale quantum error mitigation strategies for ground state preparation of the HCl molecule

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Physical Review Research 2023年第4期5卷 043054-043054页

作者： Tim Weaving Alexis Ralli William M. Kirby Peter J. Love Sauro Succi Peter V. Coveney Centre for Computational Science Department of Chemistry University College London WC1H 0AJ London United Kingdom Department of Physics and Astronomy Tufts University Medford Massachusetts 02155 USA Computational Science Initiative Brookhaven National Laboratory Upton New York 11973 USA Center for Life Nano-Neuro Science @ La Sapienza Italian Institute of Technology 00161 Roma Italy Department of Mechanical Engineering University College London WC1E 7JE London United Kingdom Department of Physics Harvard University Cambridge Massachusetts 02138 USA Advanced Research Computing Centre University College London WC1H 0AJ London United Kingdom Informatics Institute University of Amsterdam Amsterdam 1098 XH Netherlands

Due to numerous limitations including restrictive qubit topologies, short coherence times, and prohibitively high noise floors, few quantum chemistry experiments performed on existing noisy intermediate-scale quantum hardware have achieved the high bar of chemical precision, namely energy errors to within 1.6 mHa of full configuration interaction. To have any hope of doing so, we must layer contemporary resource reduction techniques with best-in-class error mitigation methods; in particular, we combine the techniques of qubit tapering and the contextual subspace variational quantum eigensolver with several error mitigation strategies comprised of measurement-error mitigation, symmetry verification, zero-noise extrapolation, and dual-state purification. We benchmark these strategies across a suite of eight 27-qubit IBM Falcon series quantum processors, taking preparation of the HCl molecule's ground state as our testbed.

关键词： Measurement-based quantum computing Quantum algorithms Quantum algorithms for chemical calculations Quantum benchmarking Quantum circuits Quantum computation Quantum gates Quantum protocols Quantum simulation Quantum software Quantum chemistry methods Variational approach

HYPER-REALIST RENDERING: A THEORETICAL FRAMEWORK

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arXiv 2024年

作者： Akleman, Ergun Kurt, Murat Akleman, Derya Bruins, Gary Deng, Sitong Subramanian, Meena Visual Computing & Computational Media School of PVFA Computer Science and Engineering College of Engineering Texas A&M University College StationTX77831 United States International Computer Institute Ege University Izmir Turkey Department of Statistics Texas A&M University College StationTX77831 United States Visualization Program Department of Architecture Texas A&M University College StationTX77831 United States Department of Visualization Texas A&M University College StationTX77831 United States

This is the first paper in a series on hyper-realist rendering. In this paper, we introduce the concept of hyper-realist rendering and present a theoretical framework to obtain hyper-realist images. We are using the term Hyper-realism as an umbrella word that captures all types of visual artifacts that can evoke an impression of reality. The hyper-realist artifacts are visual representations that are not necessarily created by following logical and physical principles and can still be perceived as representations of reality. This idea stems from the principles of representational arts, which attain visually acceptable renderings of scenes without implementing strict physical laws of optics and materials. The objective of this work is to demonstrate that it is possible to obtain visually acceptable illusions of reality by employing such artistic approaches. With representational art methods, we can even obtain an alternate illusion of reality that looks more real even when it is not real. This paper demonstrates that it is common to create illusions of reality in visual arts with examples of paintings by representational artists. We propose an approach to obtain expressive local and global illuminations to obtain these stylistic illusions with a set of well-defined and formal methods. Our formalism is based on four principles: (1) colors and textures must be represented by algebraically complete structures, (2) we should allow impossible and inconsistent shapes in scene concepts, (3) we have to decompose illumination and shading, and (4) we must build our shading functions using statistic-based methods instead of physics-based methods. We claim that these principles allow us to develop simple methods since we can avoid algebraic inconsistencies. Using such simple methods, it can be possible to significantly speed up modeling and rendering algorithms by focusing only on essential elements that can create illusions of reality. © 2024, CC BY.

关键词： Formal methods

AMG Preconditioners based on Parallel Hybrid Coarsening and Multi-objective Graph Matching

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AMG Preconditioners based on Parallel Hybrid Coarsening and ...

Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Pasqua D'Ambra Fabio Durastante S M Ferdous Salvatore Filippone Mahantesh Halappanavar Alex Pothen National Research Council (CNR) Institute for Applied Computing “M. Picone” (IAC) Napoli Italy Department of Mathematics University of Pisa Pisa Italy Pacific Northwest National Laboratory Physical and Computational Sciences Richland WA USA Dept. of Civil and Computer Engineering University of Rome Tor-Vergata Rome Italy Computer Science Department Purdue University West Lafayette IN USA

We describe preliminary results from a multi-objective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on high-end parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package PSCToolkit to exploit multi-threaded parallelism at the node level on multi-core processors. Our matching proposal balances the need to simultaneously compute high weights and large cardinalities by a new formulation of the weighted matching problem combining both these objectives using a parameter $\lambda$ . We compute the matching by a parallel $2/3-\varepsilon$ -approximation algorithm for maximum weight matchings. Results with the new matching algorithm show that for a suitable choice of the parameter $\lambda$ we compute effective preconditioners in the presence of anisotropy, i.e., smaller solve times, setup times, iterations counts, and operator complexity.

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Machine learning for elliptic PDEs: Fast rate generalization bound, neural scaling law and minimax optimality

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arXiv 2021年

作者： Lu, Yiping Chen, Haoxuan Lu, Jianfeng Ying, Lexing Blanchet, Jose Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Computing and Mathematical Sciences Caltech Mathematics Department Duke University United States Department of Mathematics Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Management Science & Engineering Stanford University StanfordCA United States

In this paper, we study the statistical limits of deep learning techniques for solving elliptic partial differential equations (PDEs) from random samples using the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). To simplify the problem, we focus on a prototype elliptic PDE: the Schrödinger equation on a hypercube with zero Dirichlet boundary condition, which has wide application in the quantum-mechanical systems. We establish upper and lower bounds for both methods, which improves upon concurrently developed upper bounds for this problem via a fast rate generalization bound. We discover that the current Deep Ritz Methods is sub-optimal and propose a modified version of it. We also prove that PINN and the modified version of DRM can achieve minimax optimal bounds over Sobolev spaces. Empirically, following recent work which has shown that the deep model accuracy will improve with growing training sets according to a power law, we supply computational experiments to show a similar behavior of dimension dependent power law for deep PDE solvers. © 2021, CC BY.

关键词： Optimization

Optimality conditions for homogeneous polynomial optimization on the unit sphere

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arXiv 2021年

作者： Huang, Lei Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

In this note, we prove that for homogeneous polynomial optimization on the sphere, if the objective f is generic in the input space, all feasible points satisfying the first order and second order necessary optimality conditions are local minimizers, which addresses an issue raised in the recent work by Lasserre (Optimization Letters, 2021). As a corollary, this implies that Lasserre’s hierarchy has finite convergence when f is generic. Copyright © 2021, The Authors. All rights reserved.

关键词： Spheres

***: A differential evolution package for the analytic continuation of imaginary time correlation functions

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arXiv 2024年

作者： Neuhaus, James Nichols, Nathan S. Banerjee, Debshikha Cohen-Stead, Benjamin Maier, Thomas A. Del Maestro, Adrian Johnston, Steven Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Institute of Advanced Materials and Manufacturing The University of Tennessee KnoxvilleTN37996 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831-6494 United States

We introduce the *** package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the *** package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://***/SmoQySuite/*** or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://***/***/stable/. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

What we should learn from pandemic publishing

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19