Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Hong, Seungbum; Liow, Chi Hao; Yuk, Jong Min; Byon, Hye Ryung; Yang, Yongsoo; Cho, EunAe; Yeom, Jiwon; Park, Gun; Kang, Hyeonmuk; Kim, Seunggu; Shim, Yoonsu; Na, Moony; Jeong, Chaehwa; Hwang, Gyuseong; Kim, Hongjun; Kim, Hoon; Eom, Seongmun; Cho, Seongwoo; Jun, Hosun; Lee, Yongju; Baucour, Arthur; Bang, Kihoon; Kim, Myungjoon; Yun, Seokjung; Ryu, Jeongjae; Han, Youngjoon; Jetybayeva, Albina; Choi, Pyuck-Pa; Agar, Joshua C.; Kalinin, Sergei V.; Voorhees, Peter W.; Littlewood, Peter; Lee, Hyuck Mo

doi:10.1021/acsnano.1c00211

Title: Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Journal Article · Fri Feb 12 00:00:00 EST 2021 · ACS Nano

DOI:https://doi.org/10.1021/acsnano.1c00211· OSTI ID:1766822

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Department of Materials Science and Engineering, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea, KAIST Institute for NanoCentury (KINC), Korea Advanced Institute of Science and Engineering (KAIST), Daejeon, 34141, Republic of Korea
Department of Materials Science and Engineering, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
Department of Chemistry, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
Department of Physics, Korea Advanced Institute of Science and Engineering (KAIST), Daejeon 34141, Republic of Korea
Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: Materials Science and Engineering Division; Office of Science User Facility, Center for Nanoph; AC05-00OR22725

OSTI ID:: 1766822

Alternate ID(s):: OSTI ID: 1785179

Journal Information:: ACS Nano, Journal Name: ACS Nano Vol. 15 Journal Issue: 3; ISSN 1936-0851

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (129)

Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos Yao, Lehan; Ou, Zihao; Luo, Binbin ACS Central Science, Vol. 6, Issue 8 https://doi.org/10.1021/acscentsci.0c00430	journal	July 2020
Building Structures Atom by Atom via Electron Beam Manipulation Dyck, Ondrej; Kim, Songkil; Jimenez-Izal, Elisa Small, Vol. 14, Issue 38 https://doi.org/10.1002/smll.201801771	journal	August 2018
Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access O’Mara, Jordan; Meredig, Bryce; Michel, Kyle JOM, Vol. 68, Issue 8 https://doi.org/10.1007/s11837-016-1984-0	journal	June 2016
GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging Pryor, Alan; Yang, Yongsoo; Rana, Arjun Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-09847-1	journal	September 2017
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Elaborately Designed Micro–Mesoporous Graphitic Carbon Spheres as Efficient Polysulfide Reservoir for Lithium–Sulfur Batteries Zheng, Jiahui; Guo, Guannan; Li, Hanwen ACS Energy Letters, Vol. 2, Issue 5 https://doi.org/10.1021/acsenergylett.7b00230	journal	April 2017
Optimizing convolutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning Stan, Tiberiu; Thompson, Zachary T.; Voorhees, Peter W. Materials Characterization, Vol. 160 https://doi.org/10.1016/j.matchar.2020.110119	journal	February 2020
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images Horwath, James P.; Zakharov, Dmitri N.; Mégret, Rémi npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00363-x	journal	July 2020
Quantitative characterization of the ionic mobility and concentration in Li-battery cathodes via low frequency electrochemical strain microscopy Alikin, D. O.; Romanyuk, K. N.; Slautin, B. N. Nanoscale, Vol. 10, Issue 5 https://doi.org/10.1039/C7NR08001H	journal	January 2018
High resolution study of domain nucleation and growth during polarization switching in Pb(Zr,Ti)O3 ferroelectric thin film capacitors Hong, Seungbum; Colla, E. L.; Kim, Eunah Journal of Applied Physics, Vol. 86, Issue 1 https://doi.org/10.1063/1.370774	journal	July 1999
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem ACS Nano, Vol. 11, Issue 12 https://doi.org/10.1021/acsnano.7b07504	journal	October 2016
Optimized electrochemical performance of Ni rich LiNi0.91Co0.06Mn0.03O2 cathodes for high-energy lithium ion batteries Lee, Seung-Hwan; Lee, Seul; Jin, Bong-Soo Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-45531-2	journal	June 2019
Data augmentation in microscopic images for material data mining Ma, Boyuan; Wei, Xiaoyan; Liu, Chuni npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00392-6	journal	August 2020
Fast inference of deep neural networks in FPGAs for particle physics Duarte, J.; Han, S.; Harris, P. Journal of Instrumentation, Vol. 13, Issue 07 https://doi.org/10.1088/1748-0221/13/07/P07027	journal	July 2018
Mastering the game of Go with deep neural networks and tree search Silver, David; Huang, Aja; Maddison, Chris J. Nature, Vol. 529, Issue 7587 https://doi.org/10.1038/nature16961	journal	January 2016
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza Computational Materials Science, Vol. 139 https://doi.org/10.1016/j.commatsci.2017.07.030	journal	November 2017
Imaging Ferroelectric Domains and Domain Walls Using Charge Gradient Microscopy: Role of Screening Charges Tong, Sheng; Jung, Il Woong; Choi, Yoon-Young ACS Nano, Vol. 10, Issue 2 https://doi.org/10.1021/acsnano.5b07551	journal	January 2016
NOMAD: The FAIR concept for big data-driven materials science Draxl, Claudia; Scheffler, Matthias MRS Bulletin, Vol. 43, Issue 9 https://doi.org/10.1557/mrs.2018.208	journal	September 2018
Local Detection of Activation Energy for Ionic Transport in Lithium Cobalt Oxide Balke, Nina; Kalnaus, Sergiy; Dudney, Nancy J. Nano Letters, Vol. 12, Issue 7 https://doi.org/10.1021/nl300219g	journal	June 2012
Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening Cheng, Lei; Assary, Rajeev S.; Qu, Xiaohui The Journal of Physical Chemistry Letters, Vol. 6, Issue 2 https://doi.org/10.1021/jz502319n	journal	January 2015
AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance Huber, Sebastiaan P.; Zoupanos, Spyros; Uhrin, Martin Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-00638-4	journal	September 2020
Urea-based hydrothermal synthesis of LiNi0.5Co0.2Mn0.3O2 cathode material for Li-ion battery Shi, Yang; Zhang, Minghao; Fang, Chengcheng Journal of Power Sources, Vol. 394 https://doi.org/10.1016/j.jpowsour.2018.05.030	journal	August 2018
Charge gradient microscopy Hong, S.; Tong, S.; Park, W. I. Proceedings of the National Academy of Sciences, Vol. 111, Issue 18 https://doi.org/10.1073/pnas.1324178111	journal	April 2014
Anomalous metal segregation in lithium-rich material provides design rules for stable cathode in lithium-ion battery Lin, Ruoqian; Hu, Enyuan; Liu, Mingjie Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-09248-0	journal	April 2019
Auto-encoder-based generative models for data augmentation on regression problems Ohno, Hiroshi Soft Computing https://doi.org/10.1007/s00500-019-04094-0	journal	May 2019
Cybermaterials: materials by design and accelerated insertion of materials Xiong, Wei; Olson, Gregory B. npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2015.9	journal	February 2016
Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures DeCost, Brian L.; Francis, Toby; Holm, Elizabeth A. Acta Materialia, Vol. 133 https://doi.org/10.1016/j.actamat.2017.05.014	journal	July 2017
Revealing the Surface Effect of the Soluble Catalyst on Oxygen Reduction/Evolution in Li–O ₂ Batteries Shen, Zhen-Zhen; Lang, Shuang-Yan; Shi, Yang Journal of the American Chemical Society, Vol. 141, Issue 17 https://doi.org/10.1021/jacs.8b12183	journal	April 2019
Strong stress-composition coupling in lithium alloy nanoparticles Seo, Hyeon Kook; Park, Jae Yeol; Chang, Joon Ha Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-11361-z	journal	July 2019
Nanoscale x-ray and electron tomography Yan, Hanfei; Voorhees, Peter W.; Xin, Huolin L. MRS Bulletin, Vol. 45, Issue 4 https://doi.org/10.1557/mrs.2020.90	journal	April 2020
Designing a New Material World Olson, G. B. Science, Vol. 288, Issue 5468 https://doi.org/10.1126/science.288.5468.993	journal	May 2000
Direct observation of region by region suppression of the switchable polarization (fatigue) in Pb(Zr,Ti)O3 thin film capacitors with Pt electrodes Colla, E. L.; Hong, Seungbum; Taylor, D. V. Applied Physics Letters, Vol. 72, Issue 21 https://doi.org/10.1063/1.121083	journal	May 1998
New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design Belsky, Alec; Hellenbrandt, Mariette; Karen, Vicky Lynn Acta Crystallographica Section B Structural Science, Vol. 58, Issue 3 https://doi.org/10.1107/S0108768102006948	journal	May 2002
Synthesis of Metallopolymers and Direct Visualization of the Single Polymer Chain Li, Zhikai; Li, Yiming; Zhao, Yiming Journal of the American Chemical Society, Vol. 142, Issue 13 https://doi.org/10.1021/jacs.0c00110	journal	March 2020
Two-Dimensional Molecular Charge Density Waves in Single-Layer-Thick Islands of a Dirac Fermion System Latt, Kyaw Zin; Schlueter, John A.; Darancet, Pierre ACS Nano, Vol. 14, Issue 7 https://doi.org/10.1021/acsnano.0c03694	journal	June 2020
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm Wu, Stephen; Kondo, Yukiko; Kakimoto, Masa-aki npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0203-2	journal	June 2019
Direct Visualization of Lithium Polysulfides and Their Suppression in Liquid Electrolyte Seo, Hyeon Kook; Hwa, Yoon; Chang, Joon Ha Nano Letters, Vol. 20, Issue 3 https://doi.org/10.1021/acs.nanolett.0c00058	journal	February 2020
In Situ Observation of Oxygen Vacancy Dynamics and Ordering in the Epitaxial LaCoO ₃ System Jang, Jae Hyuck; Kim, Young-Min; He, Qian ACS Nano, Vol. 11, Issue 7 https://doi.org/10.1021/acsnano.7b02188	journal	June 2017
The Materials Research Platform: Defining the Requirements from User Stories Aykol, Muratahan; Hummelshøj, Jens S.; Anapolsky, Abraham Matter, Vol. 1, Issue 6 https://doi.org/10.1016/j.matt.2019.10.024	journal	December 2019
Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms Choudhary, Kamal; Zhang, Qin; Reid, Andrew C. E. Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.82	journal	May 2018
Scanning Nonlinear Dielectric Microscopy Nano-Science and Technology for Next Generation High Density Ferroelectric Data Storage Tanaka, Kenkou; Kurihashi, Yuichi; Uda, Tomoya Japanese Journal of Applied Physics, Vol. 47, Issue 5 https://doi.org/10.1143/JJAP.47.3311	journal	May 2008
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches Saal, James E.; Oliynyk, Anton O.; Meredig, Bryce Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-090319-010954	journal	July 2020
The Li-Ion Rechargeable Battery: A Perspective Goodenough, John B.; Park, Kyu-Sung Journal of the American Chemical Society, Vol. 135, Issue 4 https://doi.org/10.1021/ja3091438	journal	January 2013
The Physics of Ferroelectric Memories Auciello, Orlando; Scott, James F.; Ramesh, Ramamoorthy Physics Today, Vol. 51, Issue 7 https://doi.org/10.1063/1.882324	journal	July 1998
Quantitative Electromechanical Atomic Force Microscopy Collins, Liam; Liu, Yongtao; Ovchinnikova, Olga S. ACS Nano, Vol. 13, Issue 7 https://doi.org/10.1021/acsnano.9b02883	journal	July 2019
Degradation Mechanisms and Mitigation Strategies of Nickel-Rich NMC-Based Lithium-Ion Batteries Li, Tianyu; Yuan, Xiao-Zi; Zhang, Lei Electrochemical Energy Reviews, Vol. 3, Issue 1 https://doi.org/10.1007/s41918-019-00053-3	journal	October 2019
Nanoscale mapping of ion diffusion in a lithium-ion battery cathode Balke, N.; Jesse, S.; Morozovska, A. N. Nature Nanotechnology, Vol. 5, Issue 10 https://doi.org/10.1038/nnano.2010.174	journal	August 2010
Nanostructuring one-dimensional and amorphous lithium peroxide for high round-trip efficiency in lithium-oxygen batteries Dutta, Arghya; Wong, Raymond A.; Park, Woonghyeon Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-017-02727-2	journal	February 2018
Modification and detection of domains on ferroelectric PZT films by scanning force microscopy Franke, K.; Besold, J.; Haessler, W. Surface Science, Vol. 302, Issue 1-2 https://doi.org/10.1016/0039-6028(94)91089-8	journal	January 1994
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.10	journal	December 2015
Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing Frey, Nathan C.; Akinwande, Deji; Jariwala, Deep ACS Nano, Vol. 14, Issue 10 https://doi.org/10.1021/acsnano.0c05267	journal	September 2020
Machine learning approaches for the prediction of materials properties Chibani, Siwar; Coudert, François-Xavier APL Materials, Vol. 8, Issue 8 https://doi.org/10.1063/5.0018384	journal	August 2020
Training data augmentation: An empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics Ohno, Hiroshi Applied Soft Computing, Vol. 86 https://doi.org/10.1016/j.asoc.2019.105932	journal	January 2020
Strengthening and strain hardening mechanisms in a precipitation-hardened high-Mn lightweight steel Yao, M. J.; Welsch, E.; Ponge, D. Acta Materialia, Vol. 140 https://doi.org/10.1016/j.actamat.2017.08.049	journal	November 2017
Machine Learning for High Throughput HRTEM Analysis Groschner, Catherine; Choi, Christina; Nguyen, Dat Microscopy and Microanalysis, Vol. 25, Issue S2 https://doi.org/10.1017/S143192761900148X	journal	August 2019
A reflection on lithium-ion battery cathode chemistry Manthiram, Arumugam Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-15355-0	journal	March 2020
New frontiers for the materials genome initiative de Pablo, Juan J.; Jackson, Nicholas E.; Webb, Michael A. npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0173-4	journal	April 2019
Breaking the elastic limit of piezoelectric ceramics using nanostructures: A case study using ZnO Kim, Hoon; Yun, Seokjung; Kim, Kisun Nano Energy, Vol. 78 https://doi.org/10.1016/j.nanoen.2020.105259	journal	December 2020
XEDS STEM tomography for 3D chemical characterization of nanoscale particles Genc, Arda; Kovarik, Libor; Gu, Meng Ultramicroscopy, Vol. 131 https://doi.org/10.1016/j.ultramic.2013.03.023	journal	August 2013
Towards data-driven next-generation transmission electron microscopy Spurgeon, Steven R.; Ophus, Colin; Jones, Lewys Nature Materials https://doi.org/10.1038/s41563-020-00833-z	journal	October 2020
A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ′ precipitates in Ni-based superalloys Smith, T. M.; Bonacuse, P.; Sosa, J. Materials Characterization, Vol. 140 https://doi.org/10.1016/j.matchar.2018.03.051	journal	June 2018
Membrane crystallinity and fuel crossover in direct ethanol fuel cells with Nafion composite membranes containing phosphotungstic acid Kim, Hongjun; Lee, Sunghwan; Kim, Suran Journal of Materials Science, Vol. 52, Issue 5 https://doi.org/10.1007/s10853-016-0534-z	journal	November 2016
Enhancement of Local Piezoresponse in Polymer Ferroelectrics via Nanoscale Control of Microstructure Choi, Yoon-Young; Sharma, Pankaj; Phatak, Charudatta ACS Nano, Vol. 9, Issue 2 https://doi.org/10.1021/nn5067232	journal	January 2015
The 2019 materials by design roadmap Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy Journal of Physics D: Applied Physics, Vol. 52, Issue 1 https://doi.org/10.1088/1361-6463/aad926	journal	October 2018
The Deformation and Ageing of Mild Steel: III Discussion of Results Hall, E. O. Proceedings of the Physical Society. Section B, Vol. 64, Issue 9 https://doi.org/10.1088/0370-1301/64/9/303	journal	September 1951
Closed-loop optimization of fast-charging protocols for batteries with machine learning Attia, Peter M.; Grover, Aditya; Jin, Norman Nature, Vol. 578, Issue 7795 https://doi.org/10.1038/s41586-020-1994-5	journal	February 2020
Nanoscale Visualization and Control of Ferroelectric Domains by Atomic Force Microscopy Kolosov, Oleg; Gruverman, Alexei; Hatano, Jun Physical Review Letters, Vol. 74, Issue 21 https://doi.org/10.1103/PhysRevLett.74.4309	journal	May 1995
Remarkably Improved Electrochemical Performance of Li- and Mn-Rich Cathodes upon Substitution of Mn with Ni Kumar Nayak, Prasant; Grinblat, Judith; Levi, Elena ACS Applied Materials & Interfaces, Vol. 9, Issue 5 https://doi.org/10.1021/acsami.6b07959	journal	September 2016
In situ studies of lithium-ion diffusion in a lithium-rich thin film cathode by scanning probe microscopy techniques Yang, Shan; Yan, Binggong; Li, Tao Physical Chemistry Chemical Physics, Vol. 17, Issue 34 https://doi.org/10.1039/C5CP01999K	journal	January 2015
Direct Observation of Dopant Atom Diffusion in a Bulk Semiconductor Crystal Enhanced by a Large Size Mismatch Ishikawa, Ryo; Mishra, Rohan; Lupini, Andrew R. Physical Review Letters, Vol. 113, Issue 15 https://doi.org/10.1103/PhysRevLett.113.155501	journal	October 2014
Electromechanical Imaging and Spectroscopy of Ferroelectric and Piezoelectric Materials: State of the Art and Prospects for the Future Balke, Nina; Bdikin, Igor; Kalinin, Sergei V. Journal of the American Ceramic Society, Vol. 92, Issue 8 https://doi.org/10.1111/j.1551-2916.2009.03240.x	journal	August 2009
Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets Belianinov, Alex; Vasudevan, Rama; Strelcov, Evgheni Advanced Structural and Chemical Imaging, Vol. 1, Issue 1 https://doi.org/10.1186/s40679-015-0006-6	journal	May 2015
Semi-supervised learning approaches to class assignment in ambiguous microstructures Kunselman, Courtney; Attari, Vahid; McClenny, Levi Acta Materialia, Vol. 188 https://doi.org/10.1016/j.actamat.2020.01.046	journal	April 2020
Growing field of materials informatics: databases and artificial intelligence Lopez-Bezanilla, Alejandro; Littlewood, Peter B. MRS Communications, Vol. 10, Issue 1 https://doi.org/10.1557/mrc.2020.2	journal	January 2020
Ferroelectricity in Ultrathin Perovskite Films Fong, Dillon D.; Stephenson, G. Brian; Streiffer, Stephen K. Science, Vol. 304, Issue 5677, p. 1650-1653 https://doi.org/10.1126/science.1098252	journal	June 2004
An open experimental database for exploring inorganic materials Zakutayev, Andriy; Wunder, Nick; Schwarting, Marcus Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.53	journal	April 2018
Visualization of Functional Components in a Lithium Silicon Titanium Phosphate–Natural Graphite Composite Anode Kim, Hongjun; Oh, Jimin; Park, Gun ACS Applied Energy Materials, Vol. 3, Issue 4 https://doi.org/10.1021/acsaem.9b02045	journal	March 2020
Big, Deep, and Smart Data in Scanning Probe Microscopy Kalinin, Sergei V.; Strelcov, Evgheni; Belianinov, Alex ACS Nano, Vol. 10, Issue 10 https://doi.org/10.1021/acsnano.6b04212	journal	September 2016
Scanning resistive probe microscopy: Imaging ferroelectric domains Park, Hongsik; Jung, Juhwan; Min, Dong-Ki Applied Physics Letters, Vol. 84, Issue 10 https://doi.org/10.1063/1.1667266	journal	March 2004
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes Jiang, Zhisen; Li, Jizhou; Yang, Yang Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-16233-5	journal	May 2020
Evidence for forward domain growth being rate-limiting step in polarization switching in 〈111〉-oriented-Pb(Zr0.45Ti0.55)O3 thin-film capacitors Hong, Seungbum; Setter, Nava Applied Physics Letters, Vol. 81, Issue 18 https://doi.org/10.1063/1.1517396	journal	October 2002
TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion Liu, Zhengchun; Bicer, Tekin; Kettimuthu, Rajkumar Journal of the Optical Society of America A, Vol. 37, Issue 3 https://doi.org/10.1364/JOSAA.375595	journal	January 2020
Scanning probe-type data storage beyond hard disk drive and flash memory Cho, Yasuo; Hong, Seungbum MRS Bulletin, Vol. 43, Issue 5 https://doi.org/10.1557/mrs.2018.98	journal	May 2018
Big–deep–smart data in imaging for guiding materials design Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K. Nature Materials, Vol. 14, Issue 10 https://doi.org/10.1038/nmat4395	journal	September 2015
Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr _0.2 Ti _0.8 O ₃ Thin Films Agar, Joshua C.; Cao, Ye; Naul, Brett Advanced Materials, Vol. 30, Issue 28 https://doi.org/10.1002/adma.201800701	journal	May 2018
X-ray Irradiation Induced Reversible Resistance Change in Pt/TiO ₂ /Pt Cells Chang, Seo Hyoung; Kim, Jungho; Phatak, Charudatta ACS Nano, Vol. 8, Issue 2 https://doi.org/10.1021/nn405867p	journal	January 2014
Revealing ferroelectric switching character using deep recurrent neural networks Agar, Joshua C.; Naul, Brett; Pandya, Shishir Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12750-0	journal	October 2019
Text-mined dataset of inorganic materials synthesis recipes Kononova, Olga; Huo, Haoyan; He, Tanjin Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0224-1	journal	October 2019
The high-throughput highway to computational materials design Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno Nature Materials, Vol. 12, Issue 3 https://doi.org/10.1038/nmat3568	journal	February 2013
Mastering the game of Go without human knowledge Silver, David; Schrittwieser, Julian; Simonyan, Karen Nature, Vol. 550, Issue 7676 https://doi.org/10.1038/nature24270	journal	October 2017
Deciphering chemical order/disorder and material properties at the single-atom level Yang, Yongsoo; Chen, Chien-Chun; Scott, M. C. Nature, Vol. 542, Issue 7639 https://doi.org/10.1038/nature21042	journal	February 2017
Automated defect analysis in electron microscopic images Li, Wei; Field, Kevin G.; Morgan, Dane npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0093-8	journal	July 2018
Scalable Synthesis of Honeycomb-like Ordered Mesoporous Carbon Nanosheets and Their Application in Lithium–Sulfur Batteries Park, Seung-Keun; Lee, Jeongyeon; Hwang, Taejin ACS Applied Materials & Interfaces, Vol. 9, Issue 3 https://doi.org/10.1021/acsami.6b13370	journal	January 2017
Crystallography and Databases Bruno, Ian; Gražulis, Saulius; Helliwell, John R. Data Science Journal, Vol. 16 https://doi.org/10.5334/dsj-2017-038	journal	August 2017
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials Min, Kyoungmin; Choi, Byungjin; Park, Kwangjin Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-34201-4	journal	October 2018
Insightful classification of crystal structures using deep learning Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05169-6	journal	July 2018
Strain imaging of a Pb(Zr,Ti)O ₃ thin film Takata, Keiji Journal of Applied Physics, Vol. 79, Issue 1 https://doi.org/10.1063/1.360920	journal	January 1996
Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides Young, Steven R.; Maksov, Artem; Ziatdinov, Maxim Journal of Applied Physics, Vol. 123, Issue 11 https://doi.org/10.1063/1.5009942	journal	March 2018
Imaging atomic-level random walk of a point defect in graphene Kotakoski, Jani; Mangler, Clemens; Meyer, Jannik C. Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms4991	journal	May 2014
Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities Kalinin, Sergei V.; Dyck, Ondrej; Balke, Nina ACS Nano, Vol. 13, Issue 9 https://doi.org/10.1021/acsnano.9b02687	journal	August 2019
Conflicting Roles of Nickel in Controlling Cathode Performance in Lithium Ion Batteries Gu, Meng; Belharouak, Ilias; Genc, Arda Nano Letters, Vol. 12, Issue 10 https://doi.org/10.1021/nl302249v	journal	September 2012
Data-driven discovery of coordinates and governing equations Champion, Kathleen; Lusch, Bethany; Kutz, J. Nathan Proceedings of the National Academy of Sciences, Vol. 116, Issue 45 https://doi.org/10.1073/pnas.1906995116	journal	October 2019
Nanoscale investigations of polarization in thin ferroelectric films by means of scanning force microscopy Franke, K.; Hülz, H.; Weihnacht, M. Ferroelectrics, Vol. 172, Issue 1 https://doi.org/10.1080/00150199508018503	journal	October 1995
Learning representations by back-propagating errors Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Nature, Vol. 323, Issue 6088 https://doi.org/10.1038/323533a0	journal	October 1986
Determining the Facile Routes for Oxygen Evolution Reaction by In Situ Probing of Li–O ₂ Cells with Conformal Li ₂ O ₂ Films Hong, Misun; Yang, Chunzhen; Wong, Raymond A. Journal of the American Chemical Society, Vol. 140, Issue 20 https://doi.org/10.1021/jacs.8b02003	journal	May 2018
30 Years of Lithium-Ion Batteries Li, Matthew; Lu, Jun; Chen, Zhongwei Advanced Materials, Vol. 30, Issue 33 https://doi.org/10.1002/adma.201800561	journal	June 2018
Real-Time Observation of Water-Soluble Mineral Precipitation in Aqueous Solution by In Situ High-Resolution Electron Microscopy Yuk, Jong Min; Zhou, Qin; Chang, Jiyoung ACS Nano, Vol. 10, Issue 1 https://doi.org/10.1021/acsnano.5b04064	journal	December 2015
Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls Holstad, Theodor S.; Ræder, Trygve M.; Evans, Donald M. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00426-z	journal	October 2020
A Critical Review of Machine Learning of Energy Materials Chen, Chi; Zuo, Yunxing; Ye, Weike Advanced Energy Materials, Vol. 10, Issue 8 https://doi.org/10.1002/aenm.201903242	journal	January 2020
Crystal symmetry determination in electron diffraction using machine learning Kaufmann, Kevin; Zhu, Chaoyi; Rosengarten, Alexander S. Science, Vol. 367, Issue 6477 https://doi.org/10.1126/science.aay3062	journal	January 2020
Machine-enabled inverse design of inorganic solid materials: promises and challenges Noh, Juhwan; Gu, Geun Ho; Kim, Sungwon Chemical Science, Vol. 11, Issue 19 https://doi.org/10.1039/D0SC00594K	journal	January 2020
Principle of ferroelectric domain imaging using atomic force microscope Hong, Seungbum; Woo, Jungwon; Shin, Hyunjung Journal of Applied Physics, Vol. 89, Issue 2, p. 1377-1386 https://doi.org/10.1063/1.1331654	journal	January 2001
Some Studies in Machine Learning Using the Game of Checkers Samuel, A. L. IBM Journal of Research and Development, Vol. 3, Issue 3 https://doi.org/10.1147/rd.33.0210	journal	July 1959
Measurement of hardness, surface potential, and charge distribution with dynamic contact mode electrostatic force microscope Hong, J. W.; Park, Sang-il; Khim, Z. G. Review of Scientific Instruments, Vol. 70, Issue 3 https://doi.org/10.1063/1.1149660	journal	March 1999
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Ferroelectric thin films: Review of materials, properties, and applications Setter, N.; Damjanovic, D.; Eng, L. Journal of Applied Physics, Vol. 100, Issue 5 https://doi.org/10.1063/1.2336999	journal	September 2006
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C. E. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00440-1	journal	November 2020
Visualizing nanoscale 3D compositional fluctuation of lithium in advanced lithium-ion battery cathodes Devaraj, A.; Gu, M.; Colby, R. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms9014	journal	August 2015
Phase Mapping in EBSD Using Convolutional Neural Networks Kaufmann, Kevin; Zhu, Chaoyi; Rosengarten, Alexander S. Microscopy and Microanalysis, Vol. 26, Issue 3 https://doi.org/10.1017/S1431927620001488	journal	May 2020
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
Direct imaging of the electron liquid at oxide interfaces Song, Kyung; Ryu, Sangwoo; Lee, Hyungwoo Nature Nanotechnology, Vol. 13, Issue 3 https://doi.org/10.1038/s41565-017-0040-8	journal	February 2018
High-Resolution Field Effect Sensing of Ferroelectric Charges Ko, Hyoungsoo; Ryu, Kyunghee; Park, Hongsik Nano Letters, Vol. 11, Issue 4, p. 1428-1433 https://doi.org/10.1021/nl103372a	journal	April 2011
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
The Materials Data Facility: Data Services to Advance Materials Science Research Blaiszik, B.; Chard, K.; Pruyne, J. JOM, Vol. 68, Issue 8 https://doi.org/10.1007/s11837-016-2001-3	journal	July 2016
A highly ordered nanostructured carbon–sulphur cathode for lithium–sulphur batteries Ji, Xiulei; Lee, Kyu Tae; Nazar, Linda F. Nature Materials, Vol. 8, Issue 6, p. 500-506 https://doi.org/10.1038/nmat2460	journal	May 2009
Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning Aguiar, J. A.; Gong, M. L.; Unocic, R. R. Science Advances, Vol. 5, Issue 10 https://doi.org/10.1126/sciadv.aaw1949	journal	October 2019
3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning Court, Callum J.; Yildirim, Batuhan; Jain, Apoorv Journal of Chemical Information and Modeling, Vol. 60, Issue 10 https://doi.org/10.1021/acs.jcim.0c00464	journal	August 2020
In Situ AFM Imaging of Li–O ₂ Electrochemical Reaction on Highly Oriented Pyrolytic Graphite with Ether-Based Electrolyte Wen, Rui; Hong, Misun; Byon, Hye Ryung Journal of the American Chemical Society, Vol. 135, Issue 29 https://doi.org/10.1021/ja405188g	journal	July 2013
Screening mechanisms at polar oxide heterointerfaces Hong, Seungbum; Nakhmanson, Serge M.; Fong, Dillon D. Reports on Progress in Physics, Vol. 79, Issue 7 https://doi.org/10.1088/0034-4885/79/7/076501	journal	June 2016

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