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Prognosis of Multivariate Battery State of Performance and Health via Transformers

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arXiv 2023年

作者： Paulson, Noah H. Kubal, Joseph J. Babinec, Susan J. Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Chemical Sciences and Engineering Division Argonne National Laboratory ArgonneIL60439 United States Argonne National Laboratory ArgonneIL60439 United States

Batteries are an essential component in a deeply decarbonized future. Understanding battery performance and "useful life" as a function of design and use is of paramount importance to accelerating adoption. Historically, battery state of health (SOH) was summarized by a single parameter, the fraction of a battery’s capacity relative to its initial state. A more useful approach, however, is a comprehensive characterization of its state and complexities, using an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open circuit voltages, and microstructure metrics. Indeed, predicting across an extensive suite of properties as a function of battery use is a ‘holy grail’ of battery science;it can provide unprecedented insights toward the design of better batteries with reduced experimental effort, and derisking energy storage investments that are necessary to meet CO2 reduction targets. In this work, we present a first step in that direction via deep transformer networks for the prediction of 28 battery state of health descriptors using two cycling datasets representing six lithium-ion cathode chemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions versus battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for an LFP fast-charging dataset) illustrates the promise of deep learning towards providing deeper understanding and control of battery health. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Coupling streaming AI and HPC ensembles to achieve 100–1000× faster biomolecular simulations

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Coupling streaming AI and HPC ensembles to achieve 100–1000...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Alexander Brace Igor Yakushin Heng Ma Anda Trifan Todd Munson Ian Foster Arvind Ramanathan Hyungro Lee Matteo Turilli Shantenu Jha Data Science and Learning Division Argonne National Laboratory Lemont USA Department of Computer Science University of Chicago Chicago USA Data Science and Learning Division Argonne National LaboratoryLemontUSA Center For Biophysics University of Illinois Urbana-Champaign Champaign USA Mathematics and Computer Science Division Argonne National LaboratoryLemontUSA RADICAL-Lab Rutgers University New Brunswick USA Computational Science Initiative Brookhaven National Laboratory Upton USA

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000× acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.

关键词： Proteins Couplings Computers Accelerometers Solid modeling Distributed processing Computational modeling

Pushing compute and AI onto detector silicon

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arXiv 2022年

作者： Miceli, Antonino Yoshii, Kazutomo Foster, Ian T. X-ray Science Division Argonne National Laboratory LemontIL United States Mathematics & Computer Science Division Argonne National Laboratory LemontIL United States Data Science & Learning Division Argonne National Laboratory LemontIL United States

In order to take full advantage of the U.S. Department of Energy's billion-dollar investments into the next-generation research infrastructure (e.g., exascale, light sources, colliders), advances are required not only in detector technology but also in computing and specifically AI. Let us consider an example from X-ray science. Nanoscale X-ray imaging is a crucial tool to enable a wide range of scientific explorations from materials science and biology to mechanical and civil engineering. The next-generation light sources will increase the X-ray beam brightness and coherent flux by 100 to 1,000 times. In order to image larger samples, the continuous frame rate of pixel array detectors must be increased, approaching 1 MHz, which requires several Tbps (aggregated) to transfer pixel data out to a data acquisition system. Using 65-nm CMOS technology, an optimistic raw data rate off such a chip is about 100-200 Gbps. However, a continuous 1 MHz detector with only $256 \times 256$ pixels at 16-bit resolution, for example, will require 1,000 Gbps (i.e., 1 Tbps) bandwidth off the chip! It is impractical to have multiple high-speed transceivers running in parallel to provide such bandwidth and represents the first data bottleneck. New approaches are necessary to reduce the data size by performing data compression or AI-based feature extraction directly inside a detector silicon chip in a streaming manner before sending it off-chip. Copyright © 2022, The Authors. All rights reserved.

关键词： Silicon

Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiography

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arXiv 2024年

作者： Tang, Songyuan Bicer, Tekin Sun, Tao Fezzaa, Kamel Clark, Samuel J. Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Mechanical Engineering Northwestern University EvanstonIL60208 United States

Full-field ultra-high-speed (UHS) x-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of x-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of x-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate, and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD), and structural similarity (SSIM) of the proposed framework on two independent x-ray datasets with those obtained from a baseline deep learning model, a Bayesian fusion framework, and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With 3 subsequent images from the low resolution (LR) sequence of a 4-time lower spatial resolution and another 2 images from the high resolution (HR) sequence of a 20-time lower frame rate, the proposed approach achieved an average PSNR of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore scientific value of the UHS x-ray imaging experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： X ray radiography

Structural phase transitions between layered Indium Selenide for integrated photonic memory

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arXiv 2025年

作者： Li, Tiantian Wang, Yong Li, Wei Mao, Dun Benmore, Chris J. Evangelista, Igor Xing, Huadan Li, Qiu Wang, Feifan Sivaraman, Ganesh Janotti, Anderson Law, Stephanie Gu, Tingyi Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Los Alamos National Laboratory Computer Computational and Statistical Sciences Division Los AlamosNM87545 United States X-ray Science Division Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

The primary mechanism of optical memoristive devices relies on the phase transitions between amorphous-crystalline states. The slow or energy hungry amorphous-crystalline transitions in optical phase-change materials are detrimental to the devices’ scalability and performance. Leveraging the integrated photonic platform, we demonstrate a single nanosecond pulse triggered nonvolatile and reversible switching between two layered structures of indium selenide (In2Se3). High resolution pair distribution function reveals the detailed atomistic transition pathways between the layered structures. With inter-layer ‘shear glide’ and isosymmetric phase transition, the switching between α- and β-structural states contain low re-configurational entropy, allowing reversible switching between layered structures. Broadband refractive index contrast, optical transparency, and volumetric effect in the crystalline-crystalline phase transition are experimentally characterized in molecular beam epitaxy-grown thin films and compared to ab-initial calculations. The nonlinear resonator transmission spectra measure incremental linear loss rate of 3.3 GHz introduced by 1.5 µm long In2Se3 covered layer, resulting from the combinations of material absorption and scattering. © 2025, CC BY.

关键词： Molecular beam epitaxy

Accelerating Multiscale Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine learning

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arXiv 2023年

作者： Ward, Logan Blaiszik, Ben Lee, Cheng-Wei Martin, Troy Foster, Ian Schleife, André Data Science and Learning Division Argonne National Laboratory LemontIL60437 United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States

Knowing the rate at which particle radiation releases energy in a material, the "stopping power," is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provides valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the "Bragg Peak," varies depending on incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model make our approach appealing for applications in the age of materials data science. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities 7

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Learner Action Patterns in the Problem-Solving Process Relat...

7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： data mining

Wavelength-Selective Distribution of Polarization Entanglement over Deployed Fiber

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Wavelength-Selective Distribution of Polarization Entangleme...

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Ramesh, Anirudh Chung, Joaquin Kanter, Gregory Peña, Cristián Plunkett, Robert K. Xie, Si Spentzouris, Panagiotis Kettimuthu, Rajkumar Kumar, Prem Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States NuCryptLLC 1460 Renaissance Dr #205 Park RidgeIL60068 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL United States

ISBN: (纸本)9781957171258

We demonstrate distribution of polarization entangled photon-pairs across multiple channels in a 6 km loop of deployed fiber. We use an all-optical networking switch to route Bell-state photons in three different wavelength pairs and verify entanglement distribution in all channel pairs by measuring two-photon interference fringes. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Photons

Numerical relativity higher order gravitational waveforms of eccentric, spinning, nonprecessing binary black hole mergers

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Physical Review D 2023年第6期107卷 064038-064038页

作者： Abhishek V. Joshi Shawn G. Rosofsky Roland Haas E. A. Huerta NCSA University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Computer Science University of Chicago Chicago Illinois 60637 USA

We use the open source, community-driven, numerical relativity software, the einstein toolkit to study the physics of eccentric, spinning, nonprecessing binary black hole mergers with mass-ratios q={2,4,6}, individual dimensionless spin parameters χ1z=±0.6, χ2z=±0.3, that include higher order gravitational wave modes ℓ≤4, except for memory modes. Assuming stellar mass binary black hole mergers that may be detectable by the advanced LIGO detectors, we find that including modes up to ℓ=4 increases the signal-to-noise of compact binaries between 3.5% to 35%, compared to signals that only include the ℓ=|m|=2 mode. We use two waveform models, TEOBResumS and SEOBNRE, which incorporate spin and eccentricity corrections in the waveform dynamics, to quantify the orbital eccentricity of our numerical relativity catalog in a gauge-invariant manner through fitting factor calculations. Our findings indicate that the inclusion of higher order wave modes has a measurable effect in the recovery of moderately and highly eccentric black hole mergers, and thus it is essential to develop waveform models and signal processing tools that accurately describe the physics of these astrophysical sources.

关键词： Gravitational waves Classical black holes Numerical relativity