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Unsupervised deep denoising for fourdimensional scanning transmission electron microscopy
《npj Computational Materials》2024年第1期660-672,共13页Alireza Sadri Timothy C.Petersen Emmanuel W.C.Terzoudis-Lumsden Bryan D.Esser Joanne Etheridge Scott D.Findlay 
supported under the Discovery Projects funding scheme of the Australian Research Council(Project No.FT190100619);funded by Australian Research Council grant LE170100118 and LE0454166;supported by an Australian Government Research Training Programme Scholarship.
By simultaneously achieving high spatial and angular sampling resolution,four dimensional scanning transmission electron microscopy(4DSTEM)is enabling analysis techniques that provide great insight into the atomic str...
关键词：doses enable restrict 
Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy
《npj Computational Materials》2024年第1期1508-1515,共8页Henrik Eliasson Rolf Erni 
The authors acknowledge funding from the Swiss National Science Foundation(200021_196381).
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...
关键词：COLUMNS ATOMIC SUPPORTED 
Machine learning for automated experimentation in scanning transmission electron microscopy
《npj Computational Materials》2023年第1期25-40,共16页Sergei V.Kalinin Debangshu Mukherjee Kevin Roccapriore Benjamin J.Blaiszik Ayana Ghosh Maxim A.Ziatdinov Anees Al-Najjar Christina Doty Sarah Akers Nageswara S.Rao Joshua C.Agar Steven R.Spurgeon 
Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and clo...
关键词：OPTIMIZATION AUTOMATED EXECUTION 
Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images被引量：2
《npj Computational Materials》2023年第1期1492-1500,共9页Abid Khan Chia-Hao Lee Pinshane Y.Huang Bryan K.Clark 
This work was carried out in part in the Materials Research Laboratory Central Facilities at the University of Illinois Urbana-Champaign;This research is also part of the Delta research computing project,which is supported by the National Science Foundation(award OCI 2005572),and the State of Illinois.Delta is a joint effort of the University of Illinois Urbana-Champaign and its National Center for Super-computing Applications.
The rise of automation and machine learning(ML)in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing.A significant challenge lies in developing ...
关键词：autonomous ADJUST network 
Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy被引量：5
《npj Computational Materials》2022年第1期1072-1080,共9页Chuqiao Shi Michael C.Cao Sarah M.Rehn Sang-Hoon Bae Jeehwan Kim Matthew RJones David A.Muller Yimo Han 
C.S.and Y.H.are supported by start-up funds provided by Rice University.Y.H.acknowledges the support from the Welch Foundation(C-2065-20210327);M.C.and D.A.M are supported by the NSF MRSEC program(DMR-1719875);S.M.R.would like to acknowledge financial support from a National Science Foundation Graduate Research Fellowship(No.1842494)。
Understanding lattice deformations is crucial in determining the properties of nanomaterials,which can become more prominent in future applications ranging from energy harvesting to electronic devices.However,it remai...
关键词：utilize purely OVERCOME 
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data被引量：2
《npj Computational Materials》2020年第1期570-578,共9页Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.Kalinin 
The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301;the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST;A.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extra...
关键词：FERROELECTRIC analysis FOUNDING 
Author Correction:Manifold learning of four-dimensional scanning transmission electron microscopy
《npj Computational Materials》2020年第1期1073-1073,共1页Xin Li Ondrej E.Dyck Mark P.Oxley Andrew R.Lupini Leland McInnes John Healy Stephen Jesse Sergei V.Kalinin 
The original version of the published Article had a mistake in the Acknowledgements section.The Acknowledgments have been updated to the following:This research was supported by the US Department of Energy,Basic Energ...
关键词：SECTION ledgment MANIFOLD 
Identification of stable adsorption sites and diffusion paths on nanocluster surfaces:an automated scanning algorithm
《npj Computational Materials》2019年第1期246-253,共8页Tibor Szilvási Benjamin W.J.Chen Manos Mavrikakis 
NERSC is supported by the U.S.Department of Energy,Office of Science,under contract DE-AC02-05CH11231;Work at UW-Madison was supported by Department of Energy-Basic Energy Sciences,Division of Chemical Sciences(grant DE-FG02-05ER15731);B.W.J.C.was partially supported by an Agency for Science,Technology,and Research(A*STAR)Singapore fellowship.
The diverse coordination environments on the surfaces of discrete,three-dimensional(3D)nanoclusters contribute significantly to their unique catalytic properties.Identifying the numerous adsorption sites and diffusion...
关键词：properties ADSORPTION CLUSTER 
Manifold learning of four-dimensional scanning transmission electron microscopy被引量：4
《npj Computational Materials》2019年第1期1097-1104,共8页Xin Li Ondrej E.Dyck Mark P.Oxley Andrew R.Lupini Leland McInnes John Healy Stephen Jesse Sergei V.Kalinin 
This researdh was supported by the US Department of Energy,Basic Energy Sciences,Materials Sciences and Engineering Division(M.P.O,A.R.L,S.V.K);conducted at the Center for Nanophase Materials Sciences,which is a US DOE Office of Science User Facility(X.L,O.E.D.,SJ);L.M.and J.H.acknowledge support from Tutte Institute for Mathematics and Computing,Canada.
Four-dimensional scanning transmission electron microscopy(4D-STEM)of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields....
关键词：MANIFOLD STRAIGHT DETAILS 
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