1
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Eliasson H, Erni R. Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy. NPJ COMPUTATIONAL MATERIALS 2024; 10:168. [PMID: 39104782 PMCID: PMC11297796 DOI: 10.1038/s41524-024-01360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/21/2024] [Indexed: 08/07/2024]
Abstract
To accurately capture the dynamic behavior of small nanoparticles in scanning transmission electron microscopy, high-quality data and advanced data processing is needed. The fast scan rate required to observe structural dynamics inherently leads to very noisy data where machine learning tools are essential for unbiased analysis. In this study, we develop a workflow based on two U-Net architectures to automatically localize and classify atomic columns at particle-support interfaces. The model is trained on non-physical image simulations, achieves sub-pixel localization precision, high classification accuracy, and generalizes well to experimental data. We test our model on both in situ and ex situ experimental time series recorded at 5 frames per second of small Pt nanoparticles supported on CeO2(111). The processed movies show sub-second dynamics of the nanoparticles and reveal site-specific movement patterns of individual atomic columns.
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Affiliation(s)
- Henrik Eliasson
- Electron Microscopy Center, Empa – Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Rolf Erni
- Electron Microscopy Center, Empa – Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
- Department of Materials, ETH Zürich, CH-8093 Zürich, Switzerland
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2
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Cardoso Rial R. AI in analytical chemistry: Advancements, challenges, and future directions. Talanta 2024; 274:125949. [PMID: 38569367 DOI: 10.1016/j.talanta.2024.125949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024]
Abstract
This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.
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Affiliation(s)
- Rafael Cardoso Rial
- Federal Institute of Mato Grosso do Sul, 79750-000, Nova Andradina, MS, Brazil.
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3
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Yang DH, Chu YS, Okello OFN, Seo SY, Moon G, Kim KH, Jo MH, Shin D, Mizoguchi T, Yang S, Choi SY. Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm. MATERIALS HORIZONS 2024; 11:747-757. [PMID: 37990857 DOI: 10.1039/d3mh01500a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe2: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into two unit cells elaborately cropped the unit cells: further separating a unit cell input into the Te2/Mo column part remarkably increased the defect classification accuracies. The concentrations of identified point defects were 7.16 × 1020 cm2 of Te monovacancies, 4.38 × 1019 cm2 of Te divacancies and 1.46 × 1019 cm2 of Mo monovacancies generated during an exfoliation process for TEM sample-preparation. These revealed defects correspond to the n-type character mainly originating from Te monovacancies, statistically. Our deep learning-oriented platform combined with atomic structural imaging provides the most intuitive and precise way to analyse point defects and, consequently, insight into the defect-property correlation based on deep learning in 2D materials.
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Affiliation(s)
- Dong-Hwan Yang
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
- Center for van der Waals Quantum Solids, Institute of Basic Science (IBS), 77 Cheongam-Ro, Pohang 37673, Republic of Korea
| | - Yu-Seong Chu
- Division of Biomedical Engineering, College of Health Sciences, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, 26493, Republic of Korea
| | - Odongo Francis Ngome Okello
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
| | - Seung-Young Seo
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
| | - Gunho Moon
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
- Center for van der Waals Quantum Solids, Institute of Basic Science (IBS), 77 Cheongam-Ro, Pohang 37673, Republic of Korea
| | - Kwang Ho Kim
- Department of Materials Science and Engineering, Pusan National University (PNU), 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, 46241, Busan, Republic of Korea
| | - Moon-Ho Jo
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
- Center for van der Waals Quantum Solids, Institute of Basic Science (IBS), 77 Cheongam-Ro, Pohang 37673, Republic of Korea
| | - Dongwon Shin
- Materials Science and Technology Division, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831, USA
| | - Teruyasu Mizoguchi
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 15308505, Japan
| | - Sejung Yang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, 20, Ilsan-ro, Wonju-si, Gangwon-do, Republic of Korea.
| | - Si-Young Choi
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea.
- Center for van der Waals Quantum Solids, Institute of Basic Science (IBS), 77 Cheongam-Ro, Pohang 37673, Republic of Korea
- Department of Semiconductor Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang, 37673, Republic of Korea
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4
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Gui C, Zhang Z, Li Z, Luo C, Xia J, Wu X, Chu J. Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials. iScience 2023; 26:107982. [PMID: 37810254 PMCID: PMC10551659 DOI: 10.1016/j.isci.2023.107982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
Defects are prevalent in two-dimensional (2D) materials due to thermal equilibrium and processing kinetics. The presence of various defect types can affect material properties significantly. With the development of the advanced transmission electron microscopy (TEM), the property-related structures could be investigated in multiple dimensions. It produces TEM datasets containing a large amount of information. Traditional data analysis is influenced by the subjectivity of researchers, and manual analysis is inefficient and imprecise. Recent developments in deep learning provide robust methods for the quantitative identification of defects in 2D materials efficiently and precisely. Taking advantage of big data, it breaks the limitations of TEM as a local characterization tool, making TEM an intelligent macroscopic analysis method. In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. Initially, an in-depth examination of the distinctions between TEM and natural images is presented. Subsequently, a comprehensive exploration of TEM data analysis ensues, encompassing denoising, point defects, line defects, planar defects, quantitative analysis, and applications. Furthermore, an exhaustive assessment of the significant obstacles encountered in the accurate identification of distinct structures is also provided.
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Affiliation(s)
- Chen Gui
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
| | - Zhihao Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
| | - Zongyi Li
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China
| | - Chen Luo
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Jiang Xia
- JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China
| | - Xing Wu
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Junhao Chu
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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5
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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6
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Ziatdinov M, Ghosh A, Wong CY, Kalinin SV. AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00555-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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7
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Yao L, An H, Zhou S, Kim A, Luijten E, Chen Q. Seeking regularity from irregularity: unveiling the synthesis-nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning. NANOSCALE 2022; 14:16479-16489. [PMID: 36285804 DOI: 10.1039/d2nr03712b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis-nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.
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Affiliation(s)
- Lehan Yao
- Department of Materials Science and Engineering, University of Illinois, Urbana, IL 61801, USA.
| | - Hyosung An
- Department of Materials Science and Engineering, University of Illinois, Urbana, IL 61801, USA.
- Department of Petrochemical Materials Engineering, Chonnam National University, Yeosu, 59631, Korea
| | - Shan Zhou
- Department of Materials Science and Engineering, University of Illinois, Urbana, IL 61801, USA.
| | - Ahyoung Kim
- Department of Materials Science and Engineering, University of Illinois, Urbana, IL 61801, USA.
| | - Erik Luijten
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
| | - Qian Chen
- Department of Materials Science and Engineering, University of Illinois, Urbana, IL 61801, USA.
- Department of Chemistry, University of Illinois, Urbana, IL 61801, USA
- Materials Research Laboratory, University of Illinois, Urbana, IL 61801, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL 61801, USA
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8
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Torres-Davila FE, Molinari M, Blair RG, Rochdi N, Tetard L. Enhancing Infrared Light-Matter Interaction for Deterministic and Tunable Nanomachining of Hexagonal Boron Nitride. NANO LETTERS 2022; 22:8196-8202. [PMID: 36122311 DOI: 10.1021/acs.nanolett.2c02841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Tailoring two-dimensional (2D) materials functionalities is closely intertwined with defect engineering. Conventional methods do not offer the necessary control to locally introduce and study defects in 2D materials, especially in non-vacuum environments. Here, an infrared pulsed laser focused under the metallic tip of an atomic force microscope cantilever is used to create nanoscale defects in hexagonal boron nitride (h-BN) and to subsequently investigate the induced lattice distortions by means of nanoscale infrared (nano-IR) spectroscopy. The effects of incoming light power, exposure time, and environmental conditions on the defected regions are considered. Nano-IR spectra complement the morphology maps by revealing changes in lattice vibrations that distinguish the defects formed under various environments. This work introduces versatile experimental avenues to trigger and probe local reactions that functionalize 2D materials through defect creation with a higher level of precision for applications in sensing, catalysis, optoelectronics, quantum computing, and beyond.
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Affiliation(s)
- Fernand E Torres-Davila
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Physics Department, University of Central Florida, Orlando, Florida 32816, United States
| | - Michael Molinari
- Institute of Chemistry and Biology of Membranes and Nano-objects (CBMN), CNRS UMR 5248, IPB, Université de Bordeaux, 33607 Pessac, France
| | - Richard G Blair
- Florida Space Institute, University of Central Florida, Orlando, Florida 32826, United States
- Renewable Energy and Chemical Transformations Cluster (REACT), University of Central Florida, Orlando, Florida 32816, United States
| | - Nabil Rochdi
- Laboratory of Innovative Materials, Energy and Sustainable Development (IMED-Lab), Cadi Ayyad University, Marrakesh 40000, Morocco
- Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh 40000, Morocco
| | - Laurene Tetard
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Physics Department, University of Central Florida, Orlando, Florida 32816, United States
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9
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Wang P, Ma X, Hao X, Tang B, Abudula A, Guan G. Oxygen vacancy defect engineering to promote catalytic activity toward the oxidation of VOCs: a critical review. CATALYSIS REVIEWS 2022. [DOI: 10.1080/01614940.2022.2078555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Peifen Wang
- Department of Environmental Science and Engineering, Taiyuan University of Technology, Taiyuan, P. R. China
- Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan
| | - Xuli Ma
- Department of Environmental Science and Engineering, Taiyuan University of Technology, Taiyuan, P. R. China
| | - Xiaogang Hao
- Department of Chemical Engineering, Taiyuan University of Technology, Taiyuan, P. R. China
| | - Bing Tang
- School of Environmental Science and Technology, Guangdong University of Technology, Guangzhou, P.R. China
| | - Abuliti Abudula
- Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan
| | - Guoqing Guan
- Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan
- Energy Conversion Engineering Laboratory, Institute of Regional Innovation (IRI), Hirosaki University, Hirosaki, Japan
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10
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Dan J, Zhao X, Ning S, Lu J, Loh KP, He Q, Loh ND, Pennycook SJ. Learning motifs and their hierarchies in atomic resolution microscopy. SCIENCE ADVANCES 2022; 8:eabk1005. [PMID: 35417228 PMCID: PMC9007509 DOI: 10.1126/sciadv.abk1005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
Characterizing materials to atomic resolution and first-principles structure-property prediction are two pillars for accelerating functional materials discovery. However, we are still lacking a rapid, noise-robust framework to extract multilevel atomic structural motifs from complex materials to complement, inform, and guide our first-principles models. Here, we present a machine learning framework that rapidly extracts a hierarchy of complex structural motifs from atomically resolved images. We demonstrate how such motif hierarchies can rapidly reconstruct specimens with various defects. Abstracting complex specimens with simplified motifs enabled us to discover a previously unidentified structure in a Mo─V─Te─Nb polyoxometalate (POM) and quantify the relative disorder in a twisted bilayer MoS2. In addition, these motif hierarchies provide statistically grounded clues about the favored and frustrated pathways during self-assembly. The motifs and their hierarchies in our framework coarse-grain disorder in a manner that allows us to understand a much broader range of multiscale samples with functional imperfections and nontrivial topological phases.
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Affiliation(s)
- Jiadong Dan
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 21 Lower Kent Ridge, Singapore 119077, Singapore
- Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
- NUS Centre for Bioimaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117557, Singapore
| | - Xiaoxu Zhao
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shoucong Ning
- NUS Centre for Bioimaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117557, Singapore
| | - Jiong Lu
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Kian Ping Loh
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Qian He
- Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
| | - N. Duane Loh
- NUS Centre for Bioimaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117557, Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117551, Singapore
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558, Singapore
| | - Stephen J. Pennycook
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 21 Lower Kent Ridge, Singapore 119077, Singapore
- Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
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11
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Carracedo-Cosme J, Romero-Muñiz C, Pou P, Pérez R. QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy. J Chem Inf Model 2022; 62:1214-1223. [PMID: 35234034 PMCID: PMC9942089 DOI: 10.1021/acs.jcim.1c01323] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This paper introduces Quasar Science Resources-Autonomous University of Madrid atomic force microscopy image data set (QUAM-AFM), the largest data set of simulated atomic force microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances with a different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256 × 256 pixels. The 3D stacks are especially appropriate to tackle the goal of the chemical identification within AFM experiments by using deep learning techniques. The data provided for each molecule include, besides a set of AFM images, ball-and-stick depictions, IUPAC names, chemical formulas, atomic coordinates, and map of atom heights. In order to simplify the use of the collection as a source of information, we have developed a graphical user interface that allows the search for structures by CID number, IUPAC name, or chemical formula.
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Affiliation(s)
- Jaime Carracedo-Cosme
- Quasar
Science Resources S.L., Camino de las Ceudas 2, E-28232 Las Rozas de Madrid, Spain,Departamento
de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid, Spain
| | - Carlos Romero-Muñiz
- Departamento
de Física Aplicada I, Universidad
de Sevilla, E-41012 Seville, Spain
| | - Pablo Pou
- Departamento
de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid, Spain,Condensed
Matter Physics Center (IFIMAC), Universidad
Autónoma de Madrid, E-28049 Madrid, Spain
| | - Rubén Pérez
- Departamento
de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid, E-28049 Madrid, Spain,Condensed
Matter Physics Center (IFIMAC), Universidad
Autónoma de Madrid, E-28049 Madrid, Spain,
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12
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Treder KP, Huang C, Kim JS, Kirkland AI. Applications of deep learning in electron microscopy. Microscopy (Oxf) 2022; 71:i100-i115. [DOI: 10.1093/jmicro/dfab043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/30/2021] [Accepted: 11/08/2021] [Indexed: 12/25/2022] Open
Abstract
Abstract
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.
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Affiliation(s)
- Kevin P Treder
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
| | - Chen Huang
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Judy S Kim
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Angus I Kirkland
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
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13
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Leitherer A, Ziletti A, Ghiringhelli LM. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. Nat Commun 2021; 12:6234. [PMID: 34716341 PMCID: PMC8556392 DOI: 10.1038/s41467-021-26511-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 10/04/2021] [Indexed: 12/04/2022] Open
Abstract
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.
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Affiliation(s)
- Andreas Leitherer
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany.
| | - Angelo Ziletti
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany
| | - Luca M Ghiringhelli
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany
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14
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Kalinin SV, Ziatdinov M, Hinkle J, Jesse S, Ghosh A, Kelley KP, Lupini AR, Sumpter BG, Vasudevan RK. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. ACS NANO 2021; 15:12604-12627. [PMID: 34269558 DOI: 10.1021/acsnano.1c02104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.
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15
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Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: a review. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2021; 12:878-901. [PMID: 34476169 PMCID: PMC8372315 DOI: 10.3762/bjnano.12.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/23/2021] [Indexed: 05/13/2023]
Abstract
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
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Affiliation(s)
- Ido Azuri
- Weizmann Institute of Science, Department of Life Sciences Core Facilities, Rehovot 76100, Israel
| | - Irit Rosenhek-Goldian
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
| | - Neta Regev-Rudzki
- Weizmann Institute of Science, Department of Biomolecular Sciences, Rehovot 76100, Israel
| | - Georg Fantner
- École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland
| | - Sidney R Cohen
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
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16
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Weatherley TFK, Liu W, Osokin V, Alexander DTL, Taylor RA, Carlin JF, Butté R, Grandjean N. Imaging Nonradiative Point Defects Buried in Quantum Wells Using Cathodoluminescence. NANO LETTERS 2021; 21:5217-5224. [PMID: 34086468 DOI: 10.1021/acs.nanolett.1c01295] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Crystallographic point defects (PDs) can dramatically decrease the efficiency of optoelectronic semiconductor devices, many of which are based on quantum well (QW) heterostructures. However, spatially resolving individual nonradiative PDs buried in such QWs has so far not been demonstrated. Here, using high-resolution cathodoluminescence (CL) and a specific sample design, we spatially resolve, image, and analyze nonradiative PDs in InGaN/GaN QWs at the nanoscale. We identify two different types of PDs by their contrasting behavior with temperature and measure their densities from 1014 cm-3 to as high as 1016 cm-3. Our CL images clearly illustrate the interplay between PDs and carrier dynamics in the well: increasing PD concentration severely limits carrier diffusion lengths, while a higher carrier density suppresses the nonradiative behavior of PDs. The results in this study are readily interpreted directly from CL images and represent a significant advancement in nanoscale PD analysis.
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Affiliation(s)
- Thomas F K Weatherley
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Wei Liu
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Vitaly Osokin
- Department of Physics, The Clarendon Laboratory, University of Oxford, Oxford OX1 3PU, United Kingdom
| | - Duncan T L Alexander
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Robert A Taylor
- Department of Physics, The Clarendon Laboratory, University of Oxford, Oxford OX1 3PU, United Kingdom
| | - Jean-François Carlin
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Raphaël Butté
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Nicolas Grandjean
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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17
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Abstract
Scalable quantum information systems would store, manipulate, and transmit quantum information locally and across a quantum network, but no single qubit technology is currently robust enough to perform all necessary tasks. Defect centers in solid-state materials have emerged as potential intermediaries between other physical manifestations of qubits, such as superconducting qubits and photonic qubits, to leverage their complementary advantages. It remains an open question, however, how to design and to control quantum interfaces to defect centers. Such interfaces would enable quantum information to be moved seamlessly between different physical systems. Understanding and constructing the required interfaces would, therefore, unlock the next big steps in quantum computing, sensing, and communications. In this Perspective, we highlight promising coupling mechanisms, including dipole-, phonon-, and magnon-mediated interactions, and discuss how contributions from nanotechnologists will be paramount in realizing quantum information processors in the near-term.
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Affiliation(s)
- Derek S Wang
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Michael Haas
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Prineha Narang
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
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18
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Yao X, Chen ZW, Liu GJ, Lang XY, Zhu YF, Gao W, Jiang Q. Steric Hindrance- and Work Function-Promoted High Performance for Electrochemical CO Methanation on Antisite Defects of MoS 2 and WS 2. CHEMSUSCHEM 2021; 14:2255-2261. [PMID: 33851508 DOI: 10.1002/cssc.202100457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/06/2021] [Indexed: 06/12/2023]
Abstract
CO methanation from electrochemical CO reduction reaction (CORR) is significant for sustainable environment and energy, but electrocatalysts with excellent selectivity and activity are still lacking. Selectivity is sensitive to the structure of active sites, and activity can be tailored by work function. Moreover, intrinsic active sites usually possess relatively high concentration compared to artificial ones. Here, antisite defects MoS2 and WS2 , intrinsic atomic defects of MoS2 and WS2 with a transition metal atom substituting a S2 column, were investigated for CORR by density functional theory calculations. The steric hindrance from the special bowl structure of MoS2 and WS2 ensured good selectivity towards CO methanation. Coordination environment variation of the active sites, the under-coordinated Mo or W atoms, effectively lowered the work function, making MoS2 and WS2 highly active for CO methanation with the required potential of -0.47 and -0.49 V vs. reversible hydrogen electrode, respectively. Moreover, high concentration of active sites and minimal structural deformation during the catalytic process of MoS2 and WS2 enhanced their attraction for future commercial application.
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Affiliation(s)
- Xue Yao
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Zhi-Wen Chen
- Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada
| | - Guo-Jun Liu
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Xing-You Lang
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Yong-Fu Zhu
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Wang Gao
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
| | - Qing Jiang
- Key Laboratory of Automobile Materials (Jilin University), Ministry of Education, P. R. China
- School of Materials Science and Engineering, Jilin University, Changchun, 130022, P. R. China
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19
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Qu EZ, Jimenez AM, Kumar SK, Zhang K. Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning. Macromolecules 2021. [DOI: 10.1021/acs.macromol.0c02483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Eric Zhonghang Qu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu 215300, China
| | - Andrew Matthew Jimenez
- Department of Chemical Engineering, Columbia University, New York, New York 10027, United States
| | - Sanat K. Kumar
- Department of Chemical Engineering, Columbia University, New York, New York 10027, United States
| | - Kai Zhang
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu 215300, China
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20
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Kalinin SV, Dyck O, Jesse S, Ziatdinov M. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. SCIENCE ADVANCES 2021; 7:eabd5084. [PMID: 33883126 DOI: 10.1126/sciadv.abd5084] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam-induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.
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Affiliation(s)
- Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Maxim Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
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21
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Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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22
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Wang DS, Ciccarino CJ, Flick J, Narang P. Hybridized Defects in Solid-State Materials as Artificial Molecules. ACS NANO 2021; 15:5240-5248. [PMID: 33600145 DOI: 10.1021/acsnano.0c10601] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Two-dimensional materials can be crafted with structural precision approaching the atomic scale, enabling quantum defects-by-design. These defects are frequently described as "artificial atoms" and are emerging optically addressable spin qubits. However, interactions and coupling of such artificial atoms with each other, in the presence of the lattice, warrants further investigation. Here we present the formation of "artificial molecules" in solids, introducing a chemical degree of freedom in control of quantum optoelectronic materials. Specifically, in monolayer hexagonal boron nitride as our model system, we observe configuration- and distance-dependent dissociation curves and hybridization of defect orbitals within the bandgap into bonding and antibonding orbitals, with splitting energies ranging from ∼10 meV to nearly 1 eV. We calculate the energetics of cis and trans out-of-plane defect pairs CHB-CHB against an in-plane defect pair CB-CB and find that in-plane defect pair interacts more strongly than out-of-plane pairs. We demonstrate an application of this chemical degree of freedom by varying the distance between CB and VN of CBVN and observe changes in the predicted peak absorption wavelength from the visible to the near-infrared spectral band. We envision leveraging this chemical degree of freedom of defect complexes to precisely control and tune defect properties toward engineering robust quantum memories and quantum emitters for quantum information science.
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Affiliation(s)
- Derek S Wang
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Christopher J Ciccarino
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Johannes Flick
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, United States
| | - Prineha Narang
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
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23
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Spurgeon SR, Ophus C, Jones L, Petford-Long A, Kalinin SV, Olszta MJ, Dunin-Borkowski RE, Salmon N, Hattar K, Yang WCD, Sharma R, Du Y, Chiaramonti A, Zheng H, Buck EC, Kovarik L, Penn RL, Li D, Zhang X, Murayama M, Taheri ML. Towards data-driven next-generation transmission electron microscopy. NATURE MATERIALS 2021; 20:274-279. [PMID: 33106651 PMCID: PMC8485843 DOI: 10.1038/s41563-020-00833-z] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.
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Affiliation(s)
- Steven R Spurgeon
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Colin Ophus
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Lewys Jones
- Advanced Microscopy Laboratory, Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Dublin, Ireland
- School of Physics, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | | | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Matthew J Olszta
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rafal E Dunin-Borkowski
- Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, Jülich, Germany
| | | | - Khalid Hattar
- Center for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, NM, USA
| | - Wei-Chang D Yang
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Renu Sharma
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Yingge Du
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ann Chiaramonti
- Applied Chemicals and Materials Division, Material Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, USA
| | - Haimei Zheng
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Edgar C Buck
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Libor Kovarik
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - R Lee Penn
- Department of Chemistry, University of Minnesota, Minneapolis, MN, USA
| | - Dongsheng Li
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Xin Zhang
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mitsuhiro Murayama
- Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Mitra L Taheri
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
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24
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Hassanzadeh P. The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design. Life Sci 2021; 279:119272. [PMID: 33631171 DOI: 10.1016/j.lfs.2021.119272] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/13/2022]
Abstract
Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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25
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Zhang H, Wang Y, Zuo S, Zhou W, Zhang J, Lou XWD. Isolated Cobalt Centers on W 18O 49 Nanowires Perform as a Reaction Switch for Efficient CO 2 Photoreduction. J Am Chem Soc 2021; 143:2173-2177. [PMID: 33508937 DOI: 10.1021/jacs.0c08409] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Isolated cobalt atoms have been successfully decorated onto the surface of W18O49 ultrathin nanowires. The Co-atom-decorated W18O49 nanowires (W18O49@Co) greatly accelerate the charge carrier separation and electron transport in the catalytic system. Moreover, the surface decoration with Co atoms modifies the energy configuration of the W18O49@Co hybrid and thus boosts the redox capability of photoexcited electrons for CO2 reduction. The decorated Co atoms work as the real active sites and, perhaps more importantly, perform as a reaction switch to enable the reaction to proceed. The optimized catalyst delivers considerable activity for photocatalytic CO2 reduction, yielding an impressive CO generation rate of 21.18 mmol g-1 h-1.
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Affiliation(s)
- Huabin Zhang
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Yan Wang
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Shouwei Zuo
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhou
- Department of Applied Physics, Faculty of Science, Tianjin University, Tianjin 300072, P. R. China
| | - Jing Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Xiong Wen David Lou
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
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26
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Maxim Z, Jesse S, Sumpter BG, Kalinin SV, Dyck O. Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning. NANOTECHNOLOGY 2021; 32:035703. [PMID: 32932246 DOI: 10.1088/1361-6528/abb8a6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.
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Affiliation(s)
- Ziatdinov Maxim
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
| | - Bobby G Sumpter
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States of America
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27
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Ziatdinov M, Zhang S, Dollar O, Pfaendtner J, Mundy CJ, Li X, Pyles H, Baker D, De Yoreo JJ, Kalinin SV. Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data. NANO LETTERS 2021; 21:158-165. [PMID: 33306401 DOI: 10.1021/acs.nanolett.0c03447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.
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Affiliation(s)
- Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Shuai Zhang
- Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Orion Dollar
- Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Xin Li
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Harley Pyles
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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28
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Gordon OM, Hodgkinson JEA, Farley SM, Hunsicker EL, Moriarty PJ. Automated Searching and Identification of Self-Organized Nanostructures. NANO LETTERS 2020; 20:7688-7693. [PMID: 32866019 DOI: 10.1021/acs.nanolett.0c03213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organization. Here, we use a combination of Monte Carlo simulations, general statistics, and machine learning to automatically distinguish several spatially correlated patterns in a mixed, highly varied data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organized systems and data sets.
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Affiliation(s)
- Oliver M Gordon
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Jo E A Hodgkinson
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Steff M Farley
- School of Science, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Eugénie L Hunsicker
- School of Science, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Philip J Moriarty
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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29
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Yao L, Ou Z, Luo B, Xu C, Chen Q. Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos. ACS CENTRAL SCIENCE 2020; 6:1421-1430. [PMID: 32875083 PMCID: PMC7453571 DOI: 10.1021/acscentsci.0c00430] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Indexed: 05/08/2023]
Abstract
Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos' high noisiness and spatial heterogeneity. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. We apply this framework to three typical systems of colloidal nanoparticles, concerning their diffusion and interaction, reaction kinetics, and assembly dynamics, all resolved in real-time and real-space by liquid-phase TEM. A diversity of properties for differently shaped anisotropic nanoparticles are mapped, including the anisotropic interaction landscape of nanoprisms, curvature-dependent and staged etching profiles of nanorods, and an unexpected kinetic law of first-order chaining assembly of concave nanocubes. These systems representing properties at the nanoscale are otherwise experimentally inaccessible. Compared to the prevalent image segmentation methods, U-Net shows a superior capability to predict the position and shape boundary of nanoparticles from highly noisy and fluctuating background-a challenge common and sometimes inevitable in liquid-phase TEM videos. We expect our framework to push the potency of liquid-phase TEM to its full quantitative level and to shed insights, in high-throughput and statistically significant fashion, on the nanoscale dynamics of synthetic and biological nanomaterials.
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Affiliation(s)
- Lehan Yao
- Department
of Materials Science and Engineering, Materials Research Laboratory, Beckman Institute
for Advanced Science and Technology, and Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Zihao Ou
- Department
of Materials Science and Engineering, Materials Research Laboratory, Beckman Institute
for Advanced Science and Technology, and Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Binbin Luo
- Department
of Materials Science and Engineering, Materials Research Laboratory, Beckman Institute
for Advanced Science and Technology, and Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Cong Xu
- Department
of Materials Science and Engineering, Materials Research Laboratory, Beckman Institute
for Advanced Science and Technology, and Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Qian Chen
- Department
of Materials Science and Engineering, Materials Research Laboratory, Beckman Institute
for Advanced Science and Technology, and Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- E-mail:
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30
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Kalinin SV, Dyck O, Balke N, Neumayer S, Tsai WY, Vasudevan R, Lingerfelt D, Ahmadi M, Ziatdinov M, McDowell MT, Strelcov E. Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities. ACS NANO 2019; 13:9735-9780. [PMID: 31433942 DOI: 10.1021/acsnano.9b02687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Electrochemical reactions and ionic transport underpin the operation of a broad range of devices and applications, from energy storage and conversion to information technologies, as well as biochemical processes, artificial muscles, and soft actuators. Understanding the mechanisms governing function of these applications requires probing local electrochemical phenomena on the relevant time and length scales. Here, we discuss the challenges and opportunities for extending electrochemical characterization probes to the nanometer and ultimately atomic scales, including challenges in down-scaling classical methods, the emergence of novel probes enabled by nanotechnology and based on emergent physics and chemistry of nanoscale systems, and the integration of local data into macroscopic models. Scanning probe microscopy (SPM) methods based on strain detection, potential detection, and hysteretic current measurements are discussed. We further compare SPM to electron beam probes and discuss the applicability of electron beam methods to probe local electrochemical behavior on the mesoscopic and atomic levels. Similar to a SPM tip, the electron beam can be used both for observing behavior and as an active electrode to induce reactions. We briefly discuss new challenges and opportunities for conducting fundamental scientific studies, matter patterning, and atomic manipulation arising in this context.
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Affiliation(s)
- Sergei V Kalinin
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Nina Balke
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Sabine Neumayer
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Wan-Yu Tsai
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Rama Vasudevan
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - David Lingerfelt
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Mahshid Ahmadi
- Joint Institute for Advanced Materials, Department of Materials Science and Engineering , University of Tennessee , Knoxville , Tennessee 37996 , United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Matthew T McDowell
- George W. Woodruff School of Mechanical Engineering and School of Materials Science and Engineering , Georgia Institute of Technology , Atlanta , Georgia 30332 , United States
| | - Evgheni Strelcov
- Institute for Research in Electronics and Applied Physics , University of Maryland , College Park , Maryland 20742 , United States
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31
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Vasudevan RK, Choudhary K, Mehta A, Smith R, Kusne G, Tavazza F, Vlcek L, Ziatdinov M, Kalinin SV, Hattrick-Simpers J. Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS COMMUNICATIONS 2019; 9:10.1557/mrc.2019.95. [PMID: 32166045 PMCID: PMC7067066 DOI: 10.1557/mrc.2019.95] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 07/03/2019] [Indexed: 05/14/2023]
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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Affiliation(s)
- Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Kamal Choudhary
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
| | - Ryan Smith
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Gilad Kusne
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Francesca Tavazza
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Lukas Vlcek
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Jason Hattrick-Simpers
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
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