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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

BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

High-throughput crystal structure solution using prototypes

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Physical Review Materials 2021年第10期5卷 105003-105003页

作者： Sean D. Griesemer Logan Ward Chris Wolverton Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA

databases of density functional theory (DFT) calculations, such as the Open Quantum Materials database (OQMD), have paved the way for accelerated materials discovery. DFT calculations require crystal structure information as input; however, due to inherent challenges in solving a compound's structure from powder diffraction data alone, there are thousands of experimentally synthesized compounds whose structures remain unsolved. We present a rapid DFT-based structure solution method capable of resolving numerous outstanding structure solution problems at low computational cost. The method involves (1) searching inorganic compound databases for all prototypes that match known structural characteristics of the compound, such as stoichiometry, space group, and number of atoms per cell, (2) performing DFT calculations of the target composition in each of the structural prototypes, and (3) evaluating these prototypes as candidates using a combination of DFT energy and match between calculated and experimental diffraction pattern. As this approach is straightforward and inexpensive, we employ it to solve 521 previously unsolved compounds from the Powder Diffraction File, resulting in a 1.4% expansion of the set of all experimental compounds in the OQMD. DFT calculations of these compounds could yield valuable properties.

关键词： Crystal structures Density functional theory High-throughput calculations X-ray diffraction

Real-Time Streaming and Event-driven Control of Scientific Experiments

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arXiv 2022年

作者： Elias, Jakob R. Chard, Ryan Levental, Maksim Liu, Zhengchun Foster, Ian Chaudhuri, Santanu Applied Materials Division Argonne National Laboratory United States Data Science and Learning Division Argonne National Laboratory United States Department of Computer Science University of Chicago United States University of Illinois Chicago United States

Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization, and steering. However, the diversity of sensors and heterogeneity of their data result in make it challenging to fully realize these new opportunities. Organizing and synthesizing diverse data streams in near-real-time requires both rich automation and Machine learning (ML). To efficiently utilize ML during an experiment, the entire ML lifecycle must be addressed, including refining experiment configurations, retraining models, and applying decisions—tasks that require an equally diverse array of computational resources spanning centralized HPC to the accelerators at the edge. Here we present the Manufacturing data and Machine learning platform (MDML). The MDML is designed to standardize the research and operational environment for advanced data analytics and ML-enabled automated process optimization by providing the cyberinfrastructure to integrate sensor data streams and AI in cyber-physical systems for in-situ analysis. To achieve this, the MDML provides a fabric to receive and aggregate IoT data and simultaneously orchestrate remote computation across the computing continuum. In this paper we describe the MDML and show how it is used in advanced manufacturing to act on IoT data and orchestrate distributed ML to guide experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule?

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arXiv 2024年

作者： Schwarting, Marcus Seifert, Nathan A. Davis, Michael J. Blaiszik, Ben Foster, Ian Prozument, Kirill Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Chemistry and Chemical & Biomedical Engineering University of New Haven West HavenCT06516 United States Chemical Sciences and Engineering Division Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique "fingerprint" of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy makes the testing of this assumption timely. In this paper, we pose the determination of molecular structures from rotational spectra as an inverse problem. Within this framework, we adopt a funnel-based approach to search for molecular twins, which are two or more molecules, which have similar rotational spectra but distinctly different molecular structures. We demonstrate that there are twins within standard levels of computational accuracy by generating rotational constants for many molecules from several large molecular databases, indicating the inverse problem is ill-posed. However, some twins can be distinguished by increasing the accuracy of the theoretical methods or by performing additional experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

AI forecasting of higher-order wave modes of spinning binary black hole mergers

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arXiv 2024年

作者： Tiki, Victoria Pham, Kiet Huerta, Eliu A. Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States School of Physics and Astronomy University of Minnesota Minnesota55455 United States Department of Computer Science University of Chicago ChicagoIL60637 United States

We present a physics-inspired transformer model that predicts the non-linear dynamics of higher-order wave modes emitted by quasi-circular, spinning, non-precessing binary black hole mergers. The model forecasts the waveform evolution from the pre-merger phase through to the ringdown, starting with an input time-series spanning t ∈ [−5000M, −100M). The merger event, defined as the peak amplitude of waveforms that include the = |m| = 2 modes, occurs at t = 0M. The transformer then generates predictions over the time range t ∈ [−100M, 130M]. Training, validation, and test sets were generated using the NRHybSur3dq8 model, considering a signal manifold defined by mass ratios q ∈ [1, 8];spin components sz{1,2} ∈ [−0.8, 0.8];modes up to ≤ 4, including the (5, 5) mode but excluding the (4, 0) and (4, 1) modes;and inclination angles θ ∈ [0, π]. We trained the model on 14,440,761 waveforms, completing the training in 15 hours using 16 NVIDIA A100 GPUs in the Delta supercomputer. To evaluate the model, we utilized 4 H100 GPUs in the DeltaAI supercomputer to compute, within 7 hours, the overlap between ground truth and predicted waveforms on a test set of 840,000 waveforms. The mean and median overlaps achieved were 0.996 and 0.997, respectively. Additionally, we conducted interpretability studies to elucidate the waveform features that our transformer model utilizes to produce accurate predictions. The scientific software used for this work is released with this *** Codes 68T10, 85-08, 83C35, 83C57 Copyright © 2024, The Authors. All rights reserved.

关键词： Supercomputers

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

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Transferable Graph Neural Fingerprint Models for Quick Respo...

International Conference on Machine learning and Applications (ICMLA)

作者： Wei Chen Yihui Ren Ai Kagawa Matthew R. Carbone Samuel Yen-Chi Chen Xiaohui Qu Shinjae Yoo Austin Clyde Arvind Ramanathan Rick L. Stevens Hubertus J. J. van Dam Deyu Lu Center for Functional Nanomaterials Brookhaven National Laboratory Upton NY USA Computational Science Initiative Brookhaven National Laboratory Upton NY USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Condensed Matter Physics & Materials Science Brookhaven National Laboratory Upton NY USA

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fin-gerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than 0.21 kcal/mol for most of the docking targets, showing significant improvement over conventional circular fin-gerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the training and data efficiency of the transferable model is several times higher. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.

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High-performance ptychographic reconstruction with federated facilities

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arXiv 2021年

作者： Bicer, Tekin Yu, Xiaodong Ching, Daniel J. Chard, Ryan Cherukara, Mathew J. Nicolae, Bogdan Kettimuthu, Rajkumar Foster, Ian T. Data Science and Learning Division CELS Mathematics and Computer Science Division CELS X-ray Science Division APSArgonne National Laboratory LemontIL60439 United States

Beamlines at synchrotron light source facilities are powerful scientific instruments used to image samples and observe phenomena at high spatial and temporal resolutions. Typically, these facilities are equipped only with modest compute resources for the analysis of generated experimental datasets. However, high data rate experiments can easily generate data in volumes that take days (or even weeks) to process on those local resources. To address this challenge, we present a system that unifies leadership computing and experimental facilities by enabling the automated establishment of data analysis pipelines that extend from edge data acquisition systems at synchrotron beamlines to remote computing facilities;under the covers, our system uses Globus Auth authentication to minimize user interaction, funcX to run user-defined functions on supercomputers, and Globus Flows to define and execute workflows. We describe the application of this system to ptychography, an ultrahigh-resolution coherent diffraction imaging technique that can produce 100s of gigabytes to terabytes in a single experiment. When deployed on the DGX A100 ThetaGPU cluster at the Argonne Leadership Computing Facility and a microscopy beamline at the Advanced Photon Source, our system performs analysis as an experiment progresses to provide timely feedback. Copyright © 2021, The Authors. All rights reserved.

关键词： Synchrotron radiation

JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable