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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Jinyong Ren, Yujing Tang, Baoshan Li, Changcun Liu, Fucai Gu, Deen School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Chemical and Biomolecular Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9781957171258

We present an innovative approach to optoelectronic memory that utilizes enhanced Si-based broadband lines integrated into an array to achieve stable synaptic functions, optical states and high recognition accuracy (99.64%) under near-infrared regions, paving the way for high-performance robot vision systems with advanced memory capabilities. © 2023 The Author(s)

关键词： Infrared devices

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Accelerating High-Entropy Alloy Discovery: Efficient Exploration Via Active Learning

SSRN

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SSRN 2024年

作者： Sulley, Gloria A. Raush, Jonathan Montemore, Matthew M. Hamm, Jihun Department of Chemical and Biomolecular Engineering Tulane University United States Department of Mechanical Engineering University of Louisiana Lafayette United States Department of Computer Science Tulane University United States

The exploration of the complex composition space for high entropy alloys (HEAs) is extremely challenging and resource intensive using traditional materials discovery approaches. Here, we apply Bayesian optimization in the form of active learning with a neural network model to efficiently explore the vast composition space of HEAs with a focus on predicting their stable phases. For example, by focusing data acquisition on the uncertain regions, we achieved a testing accuracy of 95% with fewer than 600 experimental datapoints, comparable to the accuracy (94.6%) of a random forest model trained with the full dataset (2198 quinary HEAs). Similarly, with just ~2% of a CALPHAD dataset we achieved a testing accuracy for phase predictions above 96%, is comparable to the accuracy (above 97%) achieved when an XGboost model is trained with the full dataset (664,650 quinary HEAs). Thus, our approach greatly facilitates both computational and experimental exploration of HEA design spaces. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings

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Procedia computer science 2025年 253卷 1412-1421页

作者： Debasish Mishra Krishna R. Pattipati George M. Bollas Data Science and Information Systems Krea University 5655 Central Expressway Sri City 517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs 06269 USA Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs 06269 USA

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices–such as Bhattacharyya distance, Fourier transform, and Cosine similarity–to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators.

关键词： Tool condition monitoring Precision machining Similarity Machine Learning Unsupervised Clustering

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Dynamics of self-hybridized exciton–polaritons in 2D halide perovskites

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Light(science & Applications) 2024年第1期13卷 13-22页

作者： Surendra B.Anantharaman Jason Lynch Christopher E.Stevens Christopher Munley Chentao Li Jin Hou Hao Zhang Andrew Torma Thomas Darlington Francis Coen Kevin Li Arka Majumdar PJames Schuck Aditya Mohite Hayk Harutyunyan Joshua R.Hendrickson Deep Jariwala Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA19104USA KBR Inc. BeavercreekOH45431USA Air Force Research Laboratory Sensors DirectorateWright-Patterson Air Force BaseOH45433USA Department of Physics University of WashingtonSeattleWA98195USA Department of Physics Emory UniversityAtlantaGA30322USA Department of Chemical and Biomolecular Engineering Rice UniversityHoustonTX77005USA Department of Materials Science and Nanoengineering Rice UniversityHoustonTX77005USA Applied Physics Program Smalley‐Curl InstituteRice UniversityHoustonTX77005USA Department of Mechanical Engineering Columbia UniversityNew YorkNY10027USA Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Excitons,bound electron–hole pairs,in two-dimensional hybrid organic inorganic perovskites(2D HOIPs)are capable of forming hybrid light-matter states known as exciton-polaritons(E–Ps)when the excitonic medium is confined in an optical *** the case of 2D HOIPs,they can self-hybridize into E–Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the ***,the fundamental properties of these self-hybridized E–Ps in 2D HOIPs,including their role in ultrafast energy and/or charge transfer at interfaces,remain ***,we demonstrate that>0.5µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E–P *** E–Ps have high Q factors(>100)and modulate the optical dispersion for the crystal to enhance sub-gap absorption and *** varying excitation energy and ultrafast measurements,we also confirm energy transfer from higher energy E–Ps to lower energy E–***,we also demonstrate that E–Ps are capable of charge transport and transfer at *** findings provide new insights into charge and energy transfer in E–Ps opening new opportunities towards their manipulation for polaritonic devices.

关键词： charge polar excitation

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Introducing Multidisciplinary engineering Technology and Programing for High School Students Through Summer Program

Introducing Multidisciplinary Engineering Technology and Pro...

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Frontiers in Education (FIE) Conference

作者： Sujing Wang Qiang Xu Stefan Andrei Department of Computer Science Lamar University Beaumont TX USA Dan F. Smith Department of Chemical and Biomolecular Engineering Lamar University Beaumont TX USA Computer Systems Engineering Technology Department Oregon Institute of Technology Klamath Falls OR USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper introduces the design, organization, and evaluation of two one-week summer camps to introduce multi-disciplinary engineering technology and programming to high school students. This program is designed to increase students' interests and knowledge of multi-disciplinary engineering and computing, foster collaboration, and build community. By engaging in our program, students not only think critically and enhance their problem-solving skills but also develop foundational proficiency in engineering and computational thinking. This crucial skill set is recognized as essential in the 21st century, opening doors to enriching engineering capabilities and creating diverse opportunities for pursuing careers in multiple engineering fields. This paper introduces the organizational structure, coordination strategies, core curriculum instructions, and the program assessment result which demonstrates a substantial improvement in knowledge and increased interest in computing and engineering across all participant cohorts. The primary objective of this paper is to furnish a detailed guide, equipping institutions with the essential information required to successfully host similar summer programs. Furthermore, it aims to catalyze endeavors geared towards enhancing young students' participation in the fields of computing and engineering.

关键词： Knowledge engineering engineering profession Collaboration Organizations Problem-solving Programming profession

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Exploring Material Design Space with a Deep-Learning Guided Genetic Algorithm 28

Exploring Material Design Space with a Deep-Learning Guided ...

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28th International Conference on DNA Computing and Molecular Programming, DNA 2022

作者： Chen, Kuan-Lin Schulman, Rebecca Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Chemistry Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783959772532

Designing complex, dynamic yet multi-functional materials and devices is challenging because the design spaces for these materials have numerous interdependent and often conflicting constraints. Taking inspiration from advances in artificial intelligence and their applications in material discovery, we propose a computational method for designing metamorphic DNA-co-polymerized hydrogel structures. The method consists of a coarse-grained simulation and a deep learning-guided optimization system for exploring the immense design space of these structures. Here, we develop a simple numeric simulation of DNA-co-polymerized hydrogel shape change and seek to find designs for structured hydrogels that can fold into the shapes of different Arabic numerals in different actuation states. We train a convolutional neural network to classify and score the geometric outputs of the coarse-grained simulation to provide autonomous feedback for design optimization. We then construct a genetic algorithm that generates and selects large batches of material designs that compete with one another to evolve and converge on optimal objective-matching designs. We show that we are able to explore the large design space and learn important parameters and traits. We identify vital relationships between the material scale size and the range of shape change that can be achieved by individual domains and we elucidate trade-offs between different design parameters. Finally, we discover material designs capable of transforming into multiple different digits in different actuation states. © Kuan-Lin Chen and Rebecca Schulman.

关键词： Genetic algorithms

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Code interoperability extends the scope of quantum simulations

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npj Computational Materials 2021年第1期7卷 302-311页

作者： Marco Govoni Jonathan Whitmer Juan de Pablo Francois Gygi Giulia Galli Materials Science Division and Center for Molecular Engineering Argonne National LaboratoryLemontIL 60439USA Department of Chemical and Biomolecular Engineering University of Notre DameNotre DameIN 46556USA Pritzker School of Molecular Engineering University of ChicagoChicagoIL 60637USA Department of Computer Science University of CaliforniaDavisCA 95616USA Department of Chemistry University of ChicagoChicagoIL 60637USA

The functionality of many materials is critically dependent on the integration of dissimilar components and on the interfaces that arise between *** description of such heterogeneous components requires the development and deployment of first principles methods,coupled to appropriate dynamical descriptions of matter and advanced sampling techniques,in order to capture all the relevant length and time scales of importance to the materials’*** is thus essential to build simple,streamlined computational schemes for the prediction and design of multiple properties of broad classes of materials,by developing interoperable codes which can be efficiently coupled to each other to perform complex *** discuss the use of interoperable codes to simulate the structural and spectroscopic characterization of materials,including chemical reactions for catalysis,the description of defects for quantum information science,and heat and charge transport.

关键词： quantum catalysis spectroscopic

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High-Efficiency and High-Current GaN-Based Microwave Rectifier for Wireless Strain Sensing and Monitoring

High-Efficiency and High-Current GaN-Based Microwave Rectifi...

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作者： Liu, Tao Li, Yang Yang, Jia-Yi Wang, Ting-Ting Wang, Xiao Yang, Lin-An Dickey, Michael Ao, Jin-Ping Hao, Yue Xidian University Key Laboratory for Wide Band-Gap Semiconductor Materials and Devices School of Microelectronics Xi'an710071 China Jiangnan University Engineering Research Center of Internet of Things Technology Applications Ministry of Education Department of Electronic Engineering Wuxi214122 China Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710064 China North Carolina State University Department of Chemical and Biomolecular Engineering RaleighNC27695 United States

In this article, a gallium nitride (GaN) Schottky barrier diode (SBD)-based microwave rectifier with high efficiency, current, and power bandwidth is proposed for wireless strain sensing and monitoring. Solutions including semiconductor devices, rectification circuits, and sensing mechanisms are discussed. A microwave rectifier working at 928 MHz is designed for wireless rectification strain sensing, with a measured power efficiency over 85% and a load resistance lower to 40 Omega based on a customized quasi-vertical GaN SBD. The finger layout and TiN anode make the SBD series resistance reduced to 1.81~Omega , but saturation current enhanced to 11.8 nA, which significantly reduces rectification loss when the load resistance is small. Finally, a rectification strain sensor (RSS) is proposed by dielectric coupling the rectifier and a piezo-permittivity liquid metal elastomer foam (LMEF), with a measured relative output power variation and wireless working distance over 437%/mm and 3 m, respectively. © 1963-2012 IEEE.

关键词： III-V semiconductors

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Symbolic regression-based hybrid models for a manufacturing process

Symbolic regression-based hybrid models for a manufacturing ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Seulki Han Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT USA Data Science & Information Systems Krea University Sri City Andhra Pradesh India Department of Electrical & Computer Engineering University of Connecticut Storrs CT USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Hybrid models are increasingly employed to simulate intricate physical processes by combining domain expertise embedded in physics-based models with process measurements-based data. The combination of domain knowledge and measurement data results in hybrid models that lead to high-accuracy decisions, extrapolation capabilities, and compliance with basic physical laws. Surrogate modeling, one aspect of hybrid modeling, involves a simplified model to capture the physics in a process, emphasizing an adaptable mathematical expression. This study applies hybrid modeling to address the intricate tool wear process in precision machining by developing a recursive model using symbolic regression.

关键词： Adaptation models Manufacturing processes Machining Predictive models Feature extraction Mathematical models Data models Time measurement Information technology Physics

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