| 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 | 373 | 2017 |
| 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 | 154 | 2019 |
| 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), 12, 2019 | 150 | 2019 |
| Learning surface molecular structures via machine vision M Ziatdinov, A Maksov, SV Kalinin npj Computational Materials 3 (1), 31, 2017 | 111 | 2017 |
| 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 | 109 | 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 | 106 | 2020 |
| Machine learning in scanning transmission electron microscopy SV Kalinin, C Ophus, PM Voyles, R Erni, D Kepaptsoglou, V Grillo, ... Nature Reviews Methods Primers 2 (1), 11, 2022 | 101 | 2022 |
| 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 | 98 | 2019 |
| Automated and autonomous experiments in electron and scanning probe microscopy SV Kalinin, M Ziatdinov, J Hinkle, S Jesse, A Ghosh, KP Kelley, AR Lupini, ... ACS nano 15 (8), 12604-12627, 2021 | 96 | 2021 |
| 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), 13774, 2016 | 92 | 2016 |
| Experimental discovery of structure–property relationships in ferroelectric materials via active learning Y Liu, KP Kelley, RK Vasudevan, H Funakubo, MA Ziatdinov, SV Kalinin Nature Machine Intelligence 4 (4), 341-350, 2022 | 88 | 2022 |
| 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 | 81 | 2014 |
| Machine learning for high-throughput experimental exploration of metal halide perovskites M Ahmadi, M Ziatdinov, Y Zhou, EA Lass, SV Kalinin Joule 5 (11), 2797-2822, 2021 | 77 | 2021 |
| AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy M Ziatdinov, A Ghosh, CY Wong, SV Kalinin Nature Machine Intelligence 4 (12), 1101-1112, 2022 | 69 | 2022 |
| 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 | 69 | 2014 |
| Hypothesis learning in automated experiment: application to combinatorial materials libraries MA Ziatdinov, Y Liu, AN Morozovska, EA Eliseev, X Zhang, I Takeuchi, ... Advanced Materials 34 (20), 2201345, 2022 | 66 | 2022 |
| 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 | 64 | 2016 |
| Exploring order parameters and dynamic processes in disordered systems via variational autoencoders SV Kalinin, O Dyck, S Jesse, M Ziatdinov Science Advances 7 (17), eabd5084, 2021 | 63 | 2021 |
| 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, 1-20, 2018 | 63 | 2018 |
| 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—Condensed Matter and Materials Physics 87 (11), 115427, 2013 | 63 | 2013 |
Sergei V. KalininWeston Fulton Chair Professor, UT Knoxville. Chief Scientist, AI/ML for Physical Sciences, PNNLAdresse e-mail validée de utk.edu