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Recent advances and applications of deep learning methods in materials science

npj Computational materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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Cooling Yb-Doped Silica Fibers and Fiber Lasers with Anti-Stokes Pumping 5

Cooling Yb-Doped Silica Fibers and Fiber Lasers with Anti-St...

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Photonic Heat Engines: science and Applications V 2023

作者： Frey, Benjamin N. Balliu, Enkeleda Vigneron, Pierre-Baptiste Knall, Jenny Hawkins, Thomas W. Meehan, Bailey Cahoon, Mary Ann Ballato, John Engholm, Magnus Dragic, Peter D. Yu, Nanjie Boilard, Tommy Bernier, Martin Digonnet, Michel J.F. Applied Physics Department Stanford University CA United States Department of Engineering Mathematics and Science Education Mid Sweden University Sundsvall Sweden Department of Materials Science and Engineering Clemson University South Carolina United States Department of Electrical Computer Engineering U. of Illinois at Urbana-Champaign IL United States Université Laval Québec Canada

ISBN: (纸本)9781510659797

Optical cooling in Yb-doped silica fibers using anti-Stokes fluorescence has become a subject of great interest in the fiber laser community. This paper provides an update on the development of silica fibers designed specifically to enhance their cooling properties. This growing list includes a new, nearly single-mode fiber with a borophosphosilicate core that produced –65 mK of cooling with only 260 mW of 1040-nm pump power. The silica compositions that have now been successfully cooled at atmospheric pressure by anti-Stokes fluorescence by our team include aluminosilicate, aluminofluorosilicate, borophosphosilicate, and aluminosilicate doped with one of three different alkali-earth nanoparticles (Ba, Sr, and Ca). By fitting the measured temperature dependence of the cooled fiber on pump power, two key parameters that control the degree of cooling are inferred, namely the critical quenching concentration and the absorptive loss due to impurities. The inferred values compiled for the fibers that cooled indicate that the extracted heat is highest when the Yb concentration is 2 wt.% or more (to maximize heat extraction), the Al concentration is ~0.8 wt.% or greater (to reduce quenching), and the absorptive loss is below approximately 15 dB/km, and ideally below 5 dB/km (to minimize heating due to pump absorption). Only two of the reported fibers, an LaF3-doped and an LuF3-doped nanoparticle fiber, did not cool, because their Yb and Al concentrations were not sufficiently high. This analysis shows that through careful composition control (especially the Al and Yb concentrations) and minimization of the OH contamination, a new generation of Yb-doped silica fibers is emerging with higher Yb concentrations, greater resistance to quenching, and lower residual loss than commercial Yb-doped fibers. They can be expected to have a significant impact not only on optically cooled devices but also on a much broader range of fiber lasers and amplifiers. © 2023 SPIE.

关键词： Nanoparticles

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Vertically Stackable Memcapacitor Crossbar Array Based on NAND Flash Array Structure

Vertically Stackable Memcapacitor Crossbar Array Based on NA...

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International Electron Devices Meeting (IEDM)

作者： Junsu Yu Hwiho Hwang Hyungjin Kim Woo Young Choi Department of Electrical and Computer Engineering Seoul National University Seoul Korea Department of Semiconductor Engineering Division of Materials Science and Engineering Hanyang University Seoul Korea

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

In this work, a vertically stackable 4F2 memcapacitor crossbar array based on charge trap flash (CTF) is experimentally demonstrated with a TiN-AI2O3-Si3N4-SiO 2 -Si (TANOS) gate stack. 4-bit multi-level operation of the fabricated 24x 48 array is verified with more than 10 years of retention and no read/write disturbance. Also, vector-matrix multiplication (VMM) operations with an error of 0.227 % are validated, along with read operations using a sensing circuit. Based on the measurement data of the planar array, the capability of performing read/write operations with a vertically stacked 3-D structure is verified through TCAD simulations. A weight transfer procedure is also provided to enhance VMM accuracy in a scaled-down vertical structure, resulting in significantly suppressed VMM errors.

关键词： Weight measurement Semiconductor device measurement Solid modeling Virtual machine monitors Voltage measurement Three-dimensional displays Capacitance-voltage characteristics Logic gates Optical variables measurement Charge measurement

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Monolithically integrated Fourier Injected REsonator (FIRE) for coherent beam combining

Monolithically integrated Fourier Injected REsonator (FIRE) ...

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CLEO: science and Innovations, S and I 2022

作者： Sweatt, Lance Zeng, Siwei Zhao, Xiaolei Corcoran, Christopher J. Zhu, Lin Department of Electrical and Computer Engineering Center for Optical Materials Science and Engineering Technologies Clemson University ClemsonSC29634 United States Corcoran Engineering Inc. United States

An on-chip FIRE resonator, consisting of an angled laser array monolithically integrated with a slab waveguide propagation region, is experimentally demonstrated for coherent beam combining with the increased supermod... 详细信息

ISBN: (纸本)9781557528209

关键词： Resonators

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ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

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IEEE Transactions on Image Processing 2023年 32卷 2843-2856页

作者： Chiou, Chien-Yu Lee, Kuang-Ting Huang, Chun-Rong Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan 701 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung 402 Taiwan National Cheng Kung University Cross College Elite Program and Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan 701 Taiwan

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://***/nchucvml/ADMM-SRNet. © 1992-2012 IEEE.

关键词： contrastive learning deep learning dictionary learning One-class classification sparse representation

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Impact of fabrication methods on binder distribution and charge transport in composite cathodes of all-solid-state batteries

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materials Futures 2023年第4期2卷 127-135页

作者： Benjamin Emley Chaoshan Wu Lihong Zhao Qing Ai Yanliang Liang Zhaoyang Chen Liqun Guo Tanguy Terlier Jun Lou Zheng Fan Yan Yao Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston4726 Calhoun RdHoustonTX 77204United States of America Department of Electrical and Computer Engineering University of Houston4726 Calhoun RdHoustonTX 77204United States of America Department of Materials Science and Nano Engineering Rice University6100 Main StreetHoustonTX 77005United States of America SIMS Laboratory Shared Equipment AuthorityRice UniversityHoustonTX 77005United States of America Department of Engineering Technology University of HoustonHoustonTX 77204United States of America

The manufacturing process of all-solid-state batteries necessitates the use of polymer ***,these binders,being ionic insulators by nature,can adversely affect charge transport within composite cathodes,thereby impacting the rate performance of the *** this work,we aim to investigate the impact of fabrication methods,specifically the solvent-free dry process versus the slurry-cast wet process,on binder distribution and charge transport in composite cathodes of solid-state *** the dry process,the binder forms a fibrous network,while the wet process results in binder coverage on the surface of cathode active *** difference in microstructure leads to a notable 20-fold increase in ionic conductivity in the dry-processed ***,the cells processed via the dry method exhibit higher capacity retention of 89%and 83%at C/3 and C/2 rates,respectively,in comparison to 68%and 58%for the wet-processed cells at the same *** findings provide valuable insights into the influence of fabrication methods on binder distribution and charge transport,contributing to a better understanding of the binder’s role in manufacturing of all-solid-state batteries.

关键词： solid-state battery solvent-free dry process polymer binder lithium thiophosphate electrolyte

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Effect of composition and two-step annealing on Ce-doped terbium iron garnets on Si

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Optical materials Express 2025年第7期15卷 1407-1418页

作者： Pang-Hsiao Liu Biazid Kabir Moghal Bethanie J H Stadler Department of Chemical Engineering and Materials Science University of Minnesota Minneapolis 55454 Minnesota USA liu01908@umn.edu Department of Electrical and Computer Engineering University of Minnesota Minneapolis 55454 Minnesota USA

Cerium-doped terbium iron garnet (CeTbIG) thin films with varying compositions and thicknesses were deposited to determine a garnet formation region. Both grain size and Faraday rotation (FR) increased in this region as the Ce content increased until 20% of the dodecahedral sites were occupied by Ce. The high Ce content was achieved by lowering the Fe ratio with respect to the total rare earth content. Above 20% Ce, the Faraday rotation was relatively independent of composition at -830 o /cm, which is similar in magnitude to positive Faraday rotation garnets, e.g.: + 600 o /cm for undoped TbIG. Next, we found that a two-step annealing method, involving a 400°C pre-anneal followed by higher temperatures, effectively reduced the maximum temperature from 900°C to 800°C without decreasing the Faraday rotation. Finally, a Si-integrated interferometer was simulated using the stable (+) and (-) Faraday rotation materials developed in this work. The simulation identified a Si-integrated Mach Zehnder Interferometers (MZI) with “push/pull” nonreciprocal phase shifts (NRPS) of opposite signs that enable mm-scale with zero external magnetic field (field-free).

关键词： Faraday effect Mach Zehnder interferometers Phase shift Pulsed laser deposition Scanning electron microscopy Thin films

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Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Nobile, Nicholas A. Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Mills, Brian Popescu, Cosmin Constantin Callahan, Dennis Hu, Juejun Ríos Ocampo, Carlos A. Youngblood, Nathan Electrical & Computer Engineering Department University of Pittsburgh PittsburghPA15213 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Draper Scholar Program The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States

Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrication challenges and efficacy of the designs. © Optica Publishing Group 2024, © 2024 The Author(s)

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

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Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis 31

Enhancing Long-range Dependency with State Space Model and K...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lawan, Adamu Pu, Juhua Yunusa, Haruna Umar, Aliyu Lawan, Muhammad School of Computer Science and Technology Beihang University Beijing China National Research Institute for Teaching Materials in Information Science and Tech. China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computing University of Portsmouth Portsmouth United Kingdom Department of Information and Communication Technology Federal University Gusau Nigeria

ISBN: (纸本)9798891761964

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models. © 2025 Association for Computational Linguistics.

关键词： Semantics

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