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Accelerating multiscale electronic stopping power predictions with timedependent density functional theory and machine learning

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npj Computational Materials 2024年第1期10卷 993-1002页

作者： Logan Ward Ben Blaiszik Cheng-Wei Lee Troy Martin Ian Foster AndréSchleife Data Science and Learning Division Argonne National LaboratoryLemontIL60437USA Department of Computer Science The University of ChicagoChicagoIL60637USA Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaUrbanaIL61801USA

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 *** 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 *** 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 provide valuable data on how atomic details influence electronic *** 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 *** 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 the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical *** 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 *** prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

关键词： electronic quantum directions

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DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material 23

DOC-DICAM: Domain Aware One Class Defect Identification in C...

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23rd IEEE International Conference on Machine learning and Applications, ICMLA 2024

作者： Yunker, Austin Kettimuthu, Rajkumar Kral, Zachary Argonne National Laboratory Data Science and Learning Division Lemont United States Spirit AeroSystems Wichita United States

ISBN: (纸本)9798350374889

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. We show that our method can effectively identify defects and requires minimal training and inference time. © 2024 IEEE.

关键词： Ultrasonic testing

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Model and data Management for Machine learning (M2ML): Integrating Instruments, Edge and HPC for Accelerated Machine learning

Model and Data Management for Machine Learning (M2ML): Integ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Zheng, Weijian Sharma, Hemant Chard, Ryan Kenesei, Peter Park, Jun-Sang Schwarz, Nicholas Miceli, Antonino Foster, Ian T. Kettimuthu, Rajkumar Argonne National Laboratory Data Science and Learning Division LemontIL United States Argonne National Laboratory X-Ray Science Division LemontIL United States

ISBN: (纸本)9798350362480

The use of data produced by scientific instruments, such as the Advanced Photon Source Upgrade (APS-U), to train and fine-tune machine learning models is becoming increasingly challenging due to high data production rates, large data volumes, and the growing complexity of machine learning models. To address these challenges, researchers have developed frameworks like fairDMS to efficiently organize vast amounts of data and models for rapid querying when model degradation is detected. However, the complexity of these frameworks and the physically distributed nature of experimental facilities complicate their *** we introduce a high-performance model and data management framework for machine learning, M2ML. In contrast to previous frameworks, M2ML abstracts the tasks into three key elements that can be easily called and accessed by users. M2ML is capable of utilizing a variety of computational resources, that are distributed across scientific facilities, to accelerate machine learning tasks. For example, it can automatically transfer data from an experimental facility (such as APS-U) to a high performance computing (HPC) facility (such as the Argonne Leadership Computing Facility (ALCF)), train machine learning models at the HPC facility, and deploy the trained models on edge computing devices back at the experimental facility for inferencing. M2ML provides a unified interface for (on-the-fly) model (re)training, storage, evaluation, fine-tuning, and inferencing using heterogeneous resources that can be geographically distributed. M2ML uses Globus services such as Globus Transfer and Globus Compute (formerly FuncX). We evaluate M2ML using a high energy diffraction microscopy (HEDM) workflow that employs BraggNN to predict the diffraction peak locations. Results show that, although the BraggNN model is small, M2ML can significantly accelerate the workflow through selective assignment of tasks to different computing resources. © 2024 IEEE.

关键词： Information management

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System for Identifying and Avoiding Obstructions on Railway Tracks in Forest Areas 10

System for Identifying and Avoiding Obstructions on Railway ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemanthkumar, P.B. Benjamin, Ruth Moly Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

Railways often faces the challenge of obstructions on the tracks especially passing through the forest area as not much precautions or announcements will be given to there also animals won't be able to understand the arrival of the train. Which results a significant safety risk to trains, travelling passengers and intruding creatures. This paper addresses for identifying the obstacles on railway tracks especially in forest areas which helps to take necessary actions to avoid those obstructions above. The system needs a camera, that has to be installed on the adjacent sides of the railway path which captures the real time data of the track. This captured data is then processed and analyzed to identify any potential obstruction on the tracks using Convolutional Neural Networks You Only Look Once version 8 is used. YOLOv8 is a high speed and accurate model;is trained with a custom dataset which is proposed in this paper. If any obstruction is detected it will inform to the train pilot and nearest stations for testing purpose mailing service is used which helps in taking necessary actions. With appropriate alerts will improve safety and efficiency on railway tracks in the forest areas by providing real time information about potential obstructions on the tracks. This directly helps in preventing accidents and delays. Ensures a safer and more reliable railway network. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Radiological Anomaly Detection using Optimized YOLO-NAS Model

Enhanced Radiological Anomaly Detection using Optimized YOLO...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Leka Shree, J. Joy, Emmanuel Sri Ram, M.S. Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Division Of Artificial Intelligence And Machine Learning Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Chest X-rays are commonly used to detect lung diseases. Chest radiographs often have a lot of perceptual errors. The application of image processing in diagnosing lung diseases has improved medical imaging. This has also enabled the creation of databases for classifying chest radiographs. However, current methods are not accurate enough to reliably detect anomalies. Aimed at improving the shortcomings of existing techniques, the proposed approach uses the Automated Neural Architecture Construction (AutoNAC) engine, a proprietary of Neural Architecture Search (NAS). Comparative evaluations against established models, including YOLOv3, YOLOv5, YOLOv7, and YOLOv8, showcase the superiority of YOLO-NAS in mean Average Precision (mAP@0.5), precision, and recall metrics. The originality of this study lies in introducing NAS specifically AutoNAC, to optimize neural network architectures for chest X-ray anomaly detection, marking a significant advancement in diagnostic capabilities in medical imaging. © 2024 IEEE.

关键词： Anomaly detection

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WebSight: An AI-based Approach to Enhance Web Accessibility for the Visually Impaired

WebSight: An AI-based Approach to Enhance Web Accessibility ...

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2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Sri Ram, M.S. Joy, Emmanuel Shree, Leka J. Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

Web accessibility plays a significant role in shaping our online experiences in today's ever-changing digital world. People with vision impairmentsstill have trouble using most websites. This paper introduces WebSight an innovative AI-based Image Description Generator web extension to address these challenges. By seamlessly interfacing with a Flask server, Optical Character Recognition, and a dedicated Image Description Generator model, this browser extension systematically enhances web accessibility by methodically replacing 'alt' attributes of images with meticulously generated descriptions. This approach significantly contributes to web inclusivity, providing visually impaired users with accurate and contextually rich information. The extension's commitment to inclusivity is further demonstrated through the intelligent integration of Tesseract-OCR and Text-to-Speech ensuring a novel and far-reaching impact, offering an enhanced and tailored browsing experience for users with visual impairments. © 2024 IEEE.

关键词： Optical character recognition

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Tomo2Mesh: Fast Porosity Mapping and Visualization for Synchrotron Tomography 19

Tomo2Mesh: Fast Porosity Mapping and Visualization for Synch...

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19th IEEE International Conference on e-science, e-science 2023

作者： Tekawade, Aniket Nikitin, Viktor Satapathy, Yashas Liu, Zhengchun Zhang, Xuan Kenesei, Peter Zheng, Weijian De Carlo, Francesco Foster, Ian Kettimuthu, Rajkumar Data Science and Learning Division Argonne National Laboratory United States X-ray Science Division Argonne National Laboratory United States Nuclear Science Division Argonne National Laboratory United States

ISBN: (纸本)9798350322231

Applications of X-ray computed tomography (CT) in porosity characterization of engineering materials often involve an extensive data analysis workflow. This workflow includes CT reconstruction of raw projection data, binarization, labeling, and mesh extraction. Mapping porosity in larger samples presents a significant computational challenge, as it requires processing gigabytes of raw data to extract porosity information, which becomes a critical bottleneck in the analysis. In this study, we present algorithms and an implementation of an end-to-end porosity mapping framework. Our framework processes raw projection data obtained from a synchrotron CT instrument, generating a porosity map and a visualization in the form of a triangular face mesh. To achieve this objective, we introduce a novel subset reconstruction scheme for X-ray CT, combining filtered back-projection and a convolutional neural network. This scheme allows us to reconstruct subsets of a tomography object with arbitrary shapes. Building upon this scheme, we have developed a fast and efficient framework for porosity mapping. Initially, our framework detects potential voids by performing a coarse reconstruction on down-sampled projections. Subsequently, we enhance the shape of these voids by reconstructing selected subsets from the original raw data, providing higher detail. To evaluate the performance of our framework, we measured the processing time from raw data to a triangular face mesh across multiple visualization scenarios. Our experiments were conducted on a single high-performance workstation equipped with a GPU. The results demonstrate that our framework enables the visualization of local porosity within an 8-gigavoxel CT volume (12 gigabytes raw data) in just 1 to 2 minutes. Moreover, for a larger 64-gigavoxel CT volume (100 gigabytes of raw data), the visualization can be generated within 3 to 7 minutes, showcasing the efficiency of our approach. © 2023 IEEE.

关键词： Porosity

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Predicting Drug Effects from High-Dimensional, Asymmetric Drug datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction 23

Predicting Drug Effects from High-Dimensional, Asymmetric Dr...

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23rd IEEE International Conference on Machine learning and Applications, ICMLA 2024

作者： Bose, Avishek Cong, Guojing Oak Ridge National Laboratory Learning Systems Group Data and Ai Systems Section Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350374889

Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using datasets that contain high-dimensional, asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects are impractical. Therefore, an opportunity exists to address this challenge as multitarget prediction problems and predict all drug effects at a time. We developed standard and hybrid GNNs to perform two separate tasks: multiregression for continuous values and multilabel classification for categorical values contained in our datasets. Because multilabel classification makes the target data even more sparse and introduces asymmetric label co-occurrence, learning these models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multilabel classification performances on all the given imbalanced molecular graph datasets. Using the technique, we improve the data imbalance ratio of the drug effects while protecting the datasets' integrity. Finally, we evaluate the multilabel classification performance of the best-performing hybrid GNN model on all the oversampled datasets obtained from the proposed oversampling technique. In all the evaluation metrics (i.e., precision, recall, and F1 score), this model significantly outperforms other ML models, including GNN models when they are trained on the original datasets or oversampled datasets with MLSMOTE, which is a well-known oversampling technique. © 2024 IEEE.

关键词： Prediction models

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Advances in Privacy Preserving Federated learning to Realize a Truly learning Healthcare System 6

Advances in Privacy Preserving Federated Learning to Realize...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex Argonne National Laboratory Data Science and Learning Division LemontIL United States Argonne National Laboratory Mathematics and Computer Science LemontIL United States Arizona State University School of Computing and Augmented Intelligence TempeAZ United States Argonne National Laboratory Data Science and Learning LemontIL United States

ISBN: (纸本)9798350386745

The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable. © 2024 IEEE.

关键词： Federated learning

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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