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Nanoscale chemical imaging with structured X-ray illumination

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Proceedings of the National Academy of sciences of the United States of America 2023年第49期120卷 e2314542120-e2314542120页

作者： Li, Jizhou Chen, Si Ratner, Daniel Blu, Thierry Pianetta, Piero Liu, Yijin Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park 94025 CA United States School of Data Science City University of HongKong Hong Kong X-ray Science Division Argonne National Laboratory Lemont 60439 IL United States Machine Learning Initiative SLAC National Accelerator Laboratory Menlo Park 94025 CA United States Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Walker Department of Mechanical Engineering The University of Texas at Austin Austin 78705 TX United States

High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, it encounters challenges in achieving enhanced detection sensitivity, satisfactory spatial resolution, and high experimental throughput simultaneously. In this work, based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. This method integrates a full-field transmission X-ray microscope with an X-ray fluorescence detector and eliminates the need for nanoscale X-ray focusing and raster scanning. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles. Bridging the gap between structural and chemical characterizations using X-rays, this technique opens up vast opportunities in the fields of biology, environmental, and materials science, especially for radiation-sensitive samples. © 2023 the Author(s).

关键词： image reconstruction synchrotron X-rays transmission X-ray microscopy X-ray chemical imaging X-ray fluorescence

StormSeeker: A machine-learning-based mediterranean storm tracer 12th

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StormSeeker: A machine-learning-based mediterranean storm tr...

12th International Conference on Internet and Distributed Computing Systems, IDCS 2019

作者： Montella, Raffaele Di Luccio, Diana Ciaramella, Angelo Foster, Ian Science and Technologies Department University of Naples "Parthenope" Naples Italy Computer Science Department University of Chicago Chicago United States Data Science and Learning Division Argonne National Laboratory Argonne United States

ISBN: (纸本)9783030349134

The Mediterranean area is subject to a range of destructive weather events, including middle-latitudes storms, Mediterranean sub-tropical hurricane-like storms ("medicanes"), and small-scale but violent local storms. Although predicting large-scale atmosphere disturbances is a common activity in numerical weather prediction, the tasks of recognizing, identifying, and tracing trajectories of such extreme weather events within weather model outputs remains challenging. We present here a new approach to this problem, called StormSeeker, that uses machine learning techniques to recognize, classify, and trace the trajectories of severe storms in atmospheric model data. We report encouraging results detecting weather hazards in a heavy middle-latitude storm that struck the Ligurian coast in October 2018, causing disastrous damages to public infrastructure and private property. © Springer Nature Switzerland AG 2019.

关键词： Machine learning

Prediction of Diblock Copolymer Morphology via Machine learning

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arXiv 2023年

作者： Park, Hyun Yu, Boyuan Park, Juhae Sun, Ge Tajkhorshid, Emad de Pablo, Juan J. Schneider, Ludwig Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago 5640 Ellis Ave ChicagoIL60637 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Copyright © 2023, The Authors. All rights reserved.

关键词： Block copolymers

Strain-induced superfluid transition for atoms on graphene

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arXiv 2022年

作者： Kim, Sang Wook Elsayed, Mohamed Nichols, Nathan S. Lakoba, Taras Vanegas, Juan Wexler, Carlos Kotov, Valeri N. Maestro, Adrian Del Department of Physics University of Vermont BurlingtonVT05405 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy University of Missouri ColumbiaMO65211 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Bosonic atoms deposited on atomically thin substrates represent a playground for exotic quantum many-body physics due to the highly-tunable, atomic-scale nature of the interaction potentials. The ability to engineer strong interparticle interactions can lead to the emergence of complex collective atomic states of matter, not possible in the context of dilute atomic gases confined in optical lattices. While it is known that the first layer of adsorbed helium on graphene is permanently locked into a solid phase, we show by a combination of quantum Monte Carlo and mean-field techniques, that simple isotropic graphene lattice expansion effectively unlocks a large variety of two-dimensional ordered commensurate, incommensurate, cluster atomic solid, and superfluid states for adsorbed atoms. It is especially significant that an atomically thin superfluid phase of matter emerges under experimentally feasible strain values, with potentially supersolid phases in close proximity on the phase diagram. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphene

Molecular Dynamics and Machine learning Unlock Possibilities in Beauty Design—A Perspective

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arXiv 2024年

作者： Xu, Yuzhi Ni, Haowei Zhao, Fanyu Gao, Qinhui Zhao, Ziqing Chang, Chia-Hua Huo, Yanran Hu, Shiyu Zhang, Yike Grovu, Radu He, Min Zhang, John Z.H. Wang, Yuanqing Simons Center for Computational Physical Chemistry New YorkNY10003 United States Department of Chemistry New York University New YorkNY10003 United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU-ECNU Center for Computational Chemistry NYU Shanghai Shanghai200062 China Department of Digital Humanities King’s College London Strand LondonWC2R 2LS United Kingdom Division of Cardiology The First Affiliated Hospital of Nanjing Medical University Nanjing210029 China Internal Medicine Department Rhode Island Hospital Brown Univerisity Health ProvidenceRI02903 United States Xbiome Inc. CambridgeMA01451 United States Center for Data Science New York University New YorkNY10004 United States

Computational molecular design—the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research & development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products. © 2024, CC BY.

关键词： Molecular dynamics

GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging

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arXiv 2024年

作者： Pati, Sarthak Mazurek, Szymon Bakas, Spyridon Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States AGH University of Krakow Academic Computer Centre Cyfronet Krakow Poland Indiana University Melvin and Bren Simon Comprehensive Cancer Center IndianapolisIN United States Department of Radiology & Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics & Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States

Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community. © 2024, CC BY.

关键词： Generative adversarial networks

Community Action on FAIR data will Fuel a Revolution in Materials Research

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arXiv 2022年

作者： Catherine Brinson, L. Bartolo, Laura M. Blaiszik, Ben Elbert, David Foster, Ian Strachan, Alejandro Voorhees, Peter W. Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Data and Databases Center for Hierarchical Materials Design Northwestern University EvanstonIL60201 United States Globus University of Chicago ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Materials Data Facility Hopkins Extreme Materials Institute Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science University of Chicago United States Materials Engineering Purdue University West LafayetteIN47907 United States nanoHUB Purdue University West LafayetteIN47907 United States Materials Science and Engineering Northwestern University EvanstonIL60201 United States Center for Hierarchical Materials Design Northwestern University EvanstonIL60201 United States Northwestern Argonne Institute for Science and Engineering Northwestern University EvanstonIL60201 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60201 United States

Widely shared and accessible materials data are the key to a world in which accelerated materials development addresses society's greatest challenges. We present a roadmap for connected materials data to enable re...

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iGEM: a model system for team science and innovation

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arXiv 2023年

作者： Santolini, Marc Blondel, Leo Palmer, Megan J. Ward, Robert N. Jeyaram, Rathin Brink, Kathryn R. Krishna, Abhijeet Barabási, Albert-László Université Paris Cité Inserm System Engineering and Evolution Dynamics ParisF-75004 France Learning Planet Institute ParisF-75004 France Network Science Institute Department of Physics Northeastern University BostonMA02115 United States Department of Bioengineering Stanford University StanfordCA United States Center for International Security and Cooperation Stanford University StanfordCA United States School of Public Policy Georgia Institute of Technology AtlantaGA United States Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Network and Data Science Central European University Budapest Hungary

Teams are a primary source of innovation in science and technology. Rather than examining the lone genius, scholarly and policy attention has shifted to understanding how team interactions produce new and useful ideas. Yet the organizational roots of innovation remain unclear, in part because of the limitations of current data. This paper introduces the international Genetically Engineered Machine (iGEM) competition, a model system for studying team science and innovation. By combining digital laboratory notebooks with performance data from 2,406 teams over multiple years of participation, we reveal shared dynamical and organizational patterns across teams and identify features associated with team performance and success. This dataset makes visible organizational behavior that is typically hidden, and thus understudied, creating new opportunities for the science of science and innovation. © 2023, CC BY.

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End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties

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arXiv 2022年

作者： Park, Hyun Zhu, Ruijie Huerta, E.A. Chaudhuri, Santanu Tajkhorshid, Emad Cooper, Donny Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab. Copyright © 2022, The Authors. All rights reserved.

关键词： Organometallics