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From Persistent Identifiers to Digital Objects to Make Data Science More Efficient

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Data Intelligence 2019年第1期1卷 6-21页

作者： Peter Wittenburg Max Planck computing and Data facility Gießenbachstraße 285748 GarchingGermany

Data-intensive science is reality in large scientific organizations such as the Max Planck Society,but due to the inefficiency of our data practices when it comes to integrating data from different sources,many projects cannot be carried out and many researchers are *** about 80%of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data-our methods do not *** experts worldwide are looking for strategies and methods that have a potential for the *** first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers(PID)and metadata(MD).In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad *** is argued,however,that assigning PIDs is just the first *** we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed,we are close to defining Digital Objects(DOs)which could indeed indicate a solution to solve some of the basic problems in data management and *** addition to standardizing the way we assign PIDs,metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data *** could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry.A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.

关键词： Big data Data management Persistent identifiers Digital objects Data infrastructure Data intensive science

Open Science and Data Science

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Data Intelligence 2021年第1期3卷 95-105页

作者： Peter Wittenburg Max Planck computing and Data facility Gießenbachstraße 285748 GarchingGermany

Data Science(DS)as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections.“Open Science by Design”(OSD),i.e.,making artefacts such as data,metadata,models,and algorithms available and re-usable to peers and beyond as early as possible,is a pre-requisite for a flourishing DS ***,a few major aspects can be identified hampering a fast transition:(1)The classical“Open Science by Publication”(OSP)is not sufficient any longer since it serves different functions,leads to non-acceptable delays and is associated with high curation *** data lab practices towards OSD requires more fundamental changes than OSP.(2)The classical publication-oriented models for metrics,mainly informed by citations,will not work anymore since the roles of contributors are more difficult to assess and will often change,i.e.,other ways for assigning incentives and recognition need to be found.(3)The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science(OS).(4)Finally,OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain-“one data-domain”as George Strawn calls it-which is still not visible due to differences about the technological key *** therefore is a need for DS,but it will take much more time to implement it than we may have expected.

关键词： Open Science by Design Open Science by Publication Data Science Data infrastructure Digital Objects FAIR

Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials

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npj Computational Materials 2021年第1期7卷 934-941页

作者： Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta Argonne leadership computing facility Argonne National LaboratoryArgonneILUSA Collaboratory for Advanced computing and Simulations University of Southern CaliforniaLos AngelesCAUSA Department of Physics and Astronomy University of Southern CaliforniaLos AngelesCAUSA Department of Chemical Engineering and Materials Science University of Southern CaliforniaLos AngelesCAUSA Department of Computer Science University of Southern CaliforniaLos AngelesCAUSA

Mechanical behavior of 2D materials such as MoS_(2) can be tuned by the ancient art of *** and atomistic simulations show that 2D materials can be stretched more than 50%by strategic insertion of ***,designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami *** use reinforcement learning(RL)to generate a wide range of highly stretchable MoS_(2) kirigami *** RL agent is trained by a small fraction(1.45%)of molecular dynamics simulation data,randomly sampled from a search space of over 4 million candidates for MoS_(2)kirigami structures with 6 *** training,the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%,but also gains mechanistic insight to propose highly stretchable(above 40%)kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.

关键词： agent learning stretch

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

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npj Computational Materials 2021年第1期7卷 983-991页

作者： Pankaj Rajak Aravind Krishnamoorthy Ankit Mishra Rajiv Kalia Aiichiro Nakano Priya Vashishta Argonne leadership computing facility Argonne National LaboratoryArgonneILUSA Collaboratory for Advanced computing and Simulations University of Southern CaliforniaLos AngelesCAUSA Department of Chemical Engineering&Materials Science University of Southern CaliforniaLos AngelesCAUSA Department of Physics&Astronomy University of Southern CaliforniaLos AngelesCAUSA Department of Computer Science University of Southern CaliforniaLos AngelesCAUSA

Predictive materials synthesis is the primary bottleneck in realizing functional and quantum *** for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for *** use offline reinforcement learning(RL)to predict optimal synthesis schedules,i.e.,a time-sequence of reaction conditions like temperatures and concentrations,for the synthesis of semiconducting monolayer MoS2 using chemical vapor *** RL agent,trained on 10,000 computational synthesis simulations,learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline,phase-pure *** model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems,far beyond the domain of molecular dynamics simulations,making these predictions directly relevant to experimental synthesis.

关键词： materials synthesis synthesis

A data-driven reduced-order model for stiff chemical kinetics using dynamics-informed training

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Energy and AI 2024年第1期15卷 181-192页

作者： Vijayamanikandan Vijayarangan Harshavardhana A.Uranakara Shivam Barwey Riccardo Malpica Galassi Mohammad Rafi Malik Mauro Valorani Venkat Raman Hong G.Im CCRC King Abdullah University of Science and TechnologyThuwalSaudi Arabia Argonne leadership computing facility Argonne National LaboratoryLemontIL 60439USA Mechanical and Aerospace Engineering Department Sapienza University of RomeVia Eudossiana18Rome 00184Italy Department of Aerospace Engineering University of MichiganAnn ArborMI 48109USA

A data-based reduced-order model(ROM)is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time ***,the objective of this work is to develop a ROM that acts as a non-stiff surrogate model for the time evolution of the thermochemical state vector(temperature and species mass fractions)during an otherwise highly stiff and nonlinear ignition *** model follows an encode-forecast-decode strategy that combines a nonlinear autoencoder(AE)for dimensionality reduction(encode and decode steps)with a neural ordinary differential equation(NODE)for modeling the dynamical system in the AE-provided latent space(forecasting step).By means of detailed timescale analysis by leveraging the dynamical system Jacobians,this work shows how data-based projection operators provided by autoencoders can inherently construct the latent spaces by removing unnecessary fast timescales,even more effectively than physics-based counterparts based on an eigenvalue analysis.A key finding is that the most significant degree of stiffness reduction is achieved through an end-to-end training strategy,where both AE and neural ODE parameters are optimized simultaneously,allowing the discovered latent space to be *** addition to end-to-end training,this work highlights the vital contribution of AE nonlinearity in the stiffness reduction *** the prediction of homogeneous ignition phenomena for H2-air and C2H4-air mixtures,the proposed ROM achieves several ordersof-magnitude increase in the integration time step size when compared to(a)a baseline CVODE solver for the full-chemical system,(b)statistical technique–principal component analysis(PCA),and(c)computational singular perturbation(CSP),a vetted physics-based stiffness-reducing modeling framework.

关键词： Stiff system Chemical kinetics Reacting flows Autoencoders Neural ODE

Numerical Experiments of Subsonic Jet Flow Simulations Using RANS with OpenFOAM

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Open Journal of Fluid Dynamics 2022年第2期12卷 230-247页

作者： Negar Naghash Zadeh Roberto Paoli Department of Mechanical and Industrial Engineering University of Illinois at Chicago Chicago USA Argonne National Laboratory Leadership Computing Facility and Computational Science Division Lemont USA

This study investigates the accuracy of different numerical schemes of OpenFOAM software to simulate compressible turbulent jets. Both pressure-based schemes utilizing the implicit PIMPLE algorithm and density-based schemes relying on AUSM scheme and explicit Runge-Kutta time integration are considered. The results of the numerical tests are compared and validated against data from NASA ARN nozzle geometry. The choice of parameter setting of the schemes is discussed in depth and possible optimization strategies are proposed to increase accuracy of RANS simulations of turbulent jets.

关键词： CFD Jet Flow Axisymmetric Turbulence RANS OpenFOAM

Evaluation of Application Possibilities for Packaging Technologies in Canonical Workflows

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Data Intelligence 2022年第2期4卷 372-385页

作者： Thomas Jejkal Sabrine Chelbi Andreas Pfeil Peter Wittenburg Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany Max Planck computing and Data facility GieBenbachstraBe 285748 GarchingGermany

InCanonicalWorkflowFramework forResearch(CWFR)"packages"arerelevantin *** data science,workflows are in general being executed on a set of files which have been aggregated for specific purposes,such as for training a model in deep *** call this type of"package"a data collection and its aggregation and metadata description is motivated by research *** other type of"packages"relevant for CWFR are supposed to represent workflows in a self-describing and self-contained way for later *** this paper,we will review different packaging technologies and investigate their usability in the context of *** this purpose,we draw on an exemplary use case and show how packaging technologies can support its *** conclude that packaging technologies of different flavors help on providing inputs and outputs for workflow steps in a machine-readable way,as well as on representing a workflow and all its artifacts in a self-describing and self-contained way.

关键词： Canonical Workflow Framework for Research Packaging technologies Research data collections Packaging formats

Molecular Dynamics Simulations of DOPC Lipid Bilayers: The Effect of Lennard-Jones Parameters of Hydrocarbon Chains

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Computational Molecular Bioscience 2012年第3期2卷 78-82页

作者： Anping Liu Xiaoyang Qi Department of Internal Medicine University of Cincinnati College of Medicine Division of Human Genetics Department of leadership computing facility Argonne National Laboratory Argonne IL USA

The current Chemistry at Harvard Molecular Mechanics (CHARMM) force field cannot accurately describe the properties of unsaturated phospholipid membranes. In this paper, a series of simulations was performed in which the Lennard- Jones (L-J) parameters of lipid acyl chains of dioleoylphosphatidylcholine (DOPC) were systematically adjusted. The results showed that adjustment of the L-J parameters in lipid acyl chains can significantly improve the current CHARMM force field. It was found that the L-J parameters have different influences on the order parameters of the top half and bottom half of the chain, separated by the cis double bond. The order parameters of the top half and the bottom half of the chain are related to the area/lipid and the length of the chain, respectively.

关键词： Molecular Dynamics Simulations DOPC Lipid Bilayers

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Editors’Note

Data Intelligence 2021年第1期3卷 1-4页

作者： Peter Wittenburg George Strawn Max Planck computing and Data facility Gießenbachstraße 285748 GarchingGermany US National Academy of Sciences Washington DC 20418USA

In 2019 the German Leibniz research organization sponsored a conference on Open Science(OS)with the idea to publish some of the presented papers in the Data Intelligence *** engaged as editors,we recognized that the term“Open Science”was coined about 10 years ago with the intention as pointed out by Michael Nielson:“OS is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process”.

关键词： pointed Open journal