Artificial-intelligence-driven scanning probe microscopy
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[1] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[2] G. Binnig,et al. Scanning tunneling microscopy-from birth to adolescence , 1987 .
[3] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .
[4] M F Crommie,et al. Confinement of Electrons to Quantum Corrals on a Metal Surface , 1993, Science.
[5] J. Villarrubia. Algorithms for Scanned Probe Microscope Image Simulation, Surface Reconstruction, and Tip Estimation , 1997, Journal of research of the National Institute of Standards and Technology.
[6] A. Rinzler,et al. Electronic structure of atomically resolved carbon nanotubes , 1998, Nature.
[7] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[8] Peter Liljeroth,et al. Amplifying the Pacific Climate System Response to a Small 11-Year Solar Cycle Forcing , 2009, Science.
[9] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[10] M. Rao,et al. Three decades of scanning tunnelling microscopy that changed the course of surface science , 2011 .
[11] D. Sánchez-Portal,et al. Atomic-scale engineering of electrodes for single-molecule contacts. , 2011, Nature nanotechnology.
[12] F. Giessibl,et al. Revealing the Angular Symmetry of Chemical Bonds by Atomic Force Microscopy , 2012, Science.
[13] L. Kantorovich,et al. Erratum: Precise Orientation of a Single C-60 Molecule on the Tip of a Scanning Probe Microscope (vol 108, 268302, 2012) , 2012 .
[14] Leo Gross,et al. Imaging the charge distribution within a single molecule. , 2012, Nature nanotechnology.
[15] L. Kantorovich,et al. Precise orientation of a single C60 molecule on the tip of a scanning probe microscope. , 2012, Physical review letters.
[16] J. Barth,et al. Assessment of Scanning Tunneling Spectroscopy Modes Inspecting Electron Confinement in Surface-Confined Supramolecular Networks , 2013, Scientific Reports.
[17] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[18] FIM tips in SPM: Apex orientation and temperature considerations on atom transfer and diffusion , 2013, 1308.0714.
[19] Johannes V. Barth,et al. CORRIGENDUM: Assessment of Scanning Tunneling Spectroscopy Modes Inspecting Electron Confinement in Surface-Confined Supramolecular Networks , 2013, Scientific Reports.
[20] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[21] J. Barth,et al. Porphyrins at interfaces. , 2015, Nature chemistry.
[22] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[23] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[24] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[25] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[26] Pieter Abbeel,et al. Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.
[27] David Silver,et al. Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.
[28] Arindam Mandal,et al. Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting , 2016, INTERSPEECH.
[29] Li Wang,et al. Conformation-based signal transfer and processing at the single-molecule level. , 2017, Nature nanotechnology.
[30] Leo Gross,et al. Generation, manipulation and characterization of molecules by atomic force microscopy , 2017 .
[31] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[32] K. M. Bastiaans,et al. Robust procedure for creating and characterizing the atomic structure of scanning tunneling microscope tips , 2017, Beilstein journal of nanotechnology.
[33] C. Gerber,et al. Imaging modes of atomic force microscopy for application in molecular and cell biology. , 2017, Nature nanotechnology.
[34] Mohammad Rashidi,et al. Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning. , 2018, ACS nano.
[35] A. Extance. How atomic imaging is being pushed to its limit , 2018, Nature.
[36] Leonidas J. Guibas,et al. Taskonomy: Disentangling Task Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] K. Rusimova,et al. Regulating the femtosecond excited-state lifetime of a single molecule , 2018, Science.
[38] M. Rashidi,et al. Lithography for robust and editable atomic-scale silicon devices and memories , 2018, Nature Communications.
[39] Loic A. Royer,et al. Content-aware image restoration: pushing the limits of fluorescence microscopy , 2018, Nature Methods.
[40] Jing Wang,et al. Direct imaging of surface states hidden in the third layer of Si (111)-7 × 7 surface bypz-wave tip , 2018, Applied Physics Letters.
[41] Boyuan Huang,et al. Artificial Intelligent Atomic Force Microscope Enabled by Machine Learning , 2018, 1807.09985.
[42] A. Extance. How atomic imaging is being pushed to its limit. , 2018 .
[43] Zhenghao Li,et al. An artificial intelligence atomic force microscope enabled by machine learning. , 2018, Nanoscale.
[44] O. Gordon,et al. Scanning Probe State Recognition With Multi-Class Neural Network Ensembles , 2019, Review of Scientific Instruments.
[45] O. Gordon,et al. Embedding human heuristics in machine-learning-enabled probe microscopy , 2019, Mach. Learn. Sci. Technol..
[46] Ernst Meyer,et al. Scanning Probe Microscopy: The Lab on a Tip , 2021 .