| Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations M Ziatdinov, O Dyck, A Maksov, X Li, X Sang, K Xiao, RR Unocic, ... ACS nano 11 (12), 12742-12752, 2017 | 220 | 2017 |
| Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2 A Maksov, O Dyck, K Wang, K Xiao, DB Geohegan, BG Sumpter, ... npj Computational Materials 5 (1), 1-8, 2019 | 86 | 2019 |
| Learning surface molecular structures via machine vision M Ziatdinov, A Maksov, SV Kalinin npj Computational Materials 3 (1), 1-9, 2017 | 75 | 2017 |
| Role of edge geometry and chemistry in the electronic properties of graphene nanostructures S Fujii, M Ziatdinov, M Ohtsuka, K Kusakabe, M Kiguchi, T Enoki Faraday discussions 173, 173-199, 2014 | 69 | 2014 |
| Atomic-scale observation of structural and electronic orders in the layered compound α-RuCl3 M Ziatdinov, A Banerjee, A Maksov, T Berlijn, W Zhou, HB Cao, JQ Yan, ... Nature communications 7 (1), 1-8, 2016 | 68 | 2016 |
| Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics RK Vasudevan, K Choudhary, A Mehta, R Smith, G Kusne, F Tavazza, ... MRS communications 9 (3), 821-838, 2019 | 63 | 2019 |
| Visualization of electronic states on atomically smooth graphitic edges with different types of hydrogen termination M Ziatdinov, S Fujii, K Kusakabe, M Kiguchi, T Mori, T Enoki Physical Review B 87 (11), 115427, 2013 | 61 | 2013 |
| Direct imaging of monovacancy-hydrogen complexes in a single graphitic layer M Ziatdinov, S Fujii, K Kusakabe, M Kiguchi, T Mori, T Enoki Physical Review B 89 (15), 155405, 2014 | 56 | 2014 |
| Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study M Ziatdinov, O Dyck, X Li, BG Sumpter, S Jesse, RK Vasudevan, ... Science advances 5 (9), eaaw8989, 2019 | 53 | 2019 |
| Atom-by-atom fabrication with electron beams O Dyck, M Ziatdinov, DB Lingerfelt, RR Unocic, BM Hudak, AR Lupini, ... Nature Reviews Materials 4 (7), 497-507, 2019 | 49 | 2019 |
| Bowl inversion and electronic switching of buckybowls on gold S Fujii, M Ziatdinov, S Higashibayashi, H Sakurai, M Kiguchi Journal of the American Chemical Society 138 (37), 12142-12149, 2016 | 45 | 2016 |
| Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images RK Vasudevan, N Laanait, EM Ferragut, K Wang, DB Geohegan, K Xiao, ... npj Computational Materials 4 (1), 1-9, 2018 | 39 | 2018 |
| Phases and interfaces from real space atomically resolved data: physics-based deep data image analysis RK Vasudevan, M Ziatdinov, S Jesse, SV Kalinin Nano letters 16 (9), 5574-5581, 2016 | 39 | 2016 |
| Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform R Kannan, AV Ievlev, N Laanait, MA Ziatdinov, RK Vasudevan, S Jesse, ... Advanced Structural and Chemical Imaging 4 (1), 1-20, 2018 | 38 | 2018 |
| Deep data mining in a real space: separation of intertwined electronic responses in a lightly doped BaFe2As2 M Ziatdinov, A Maksov, L Li, AS Sefat, P Maksymovych, SV Kalinin Nanotechnology 27 (47), 475706, 2016 | 29 | 2016 |
| 167-pflops deep learning for electron microscopy: from learning physics to atomic manipulation RM Patton, JT Johnston, SR Young, CD Schuman, DD March, TE Potok, ... SC18: International Conference for High Performance Computing, Networking …, 2018 | 28 | 2018 |
| Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides SR Young, A Maksov, M Ziatdinov, Y Cao, M Burch, J Balachandran, L Li, ... Journal of Applied Physics 123 (11), 115303, 2018 | 26 | 2018 |
| Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk M Ziatdinov, O Dyck, S Jesse, SV Kalinin Advanced Functional Materials 29 (52), 1904480, 2019 | 25 | 2019 |
| Learning from imperfections: predicting structure and thermodynamics from atomic imaging of fluctuations L Vlcek, M Ziatdinov, A Maksov, A Tselev, AP Baddorf, SV Kalinin, ... ACS nano 13 (1), 718-727, 2019 | 24 | 2019 |
| Chemical robotics enabled exploration of stability in multicomponent lead halide perovskites via machine learning K Higgins, SM Valleti, M Ziatdinov, SV Kalinin, M Ahmadi ACS Energy Letters 5 (11), 3426-3436, 2020 | 23 | 2020 |
Sergei V. KalininUniversity of Tennessee, Knoxville/AmazonVerified email at ornl.gov
