[1] Lin, X. Y. et al. Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy. Nano Res. 11, 6316-6324 (2018). doi: 10.1007/s12274-018-2155-0
[2] Zhuo, Y., Mansouri Tehrani, A. & Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J. Phys. Chem. Lett. 9, 1668-1673 (2018). doi: 10.1021/acs.jpclett.8b00124
[3] Lu, S. H. et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 9, 3405 (2018). doi: 10.1038/s41467-018-05761-w
[4] Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375 (2016). doi: 10.1038/srep19375
[5] Sun, B. C., Fernandez, M. & Barnard, A. S. Machine learning for silver nanoparticle electron transfer property prediction. J. Chem. Inf. Model. 57, 2413-2423 (2017). doi: 10.1021/acs.jcim.7b00272
[6] Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 60 (2018). doi: 10.1038/s41377-018-0060-7
[7] Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 4, eaar4206, (2018). doi: 10.1126/sciadv.aar4206
[8] Ma, W., Cheng, F. & Liu, Y. M. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12, 6326-6334 (2018). doi: 10.1021/acsnano.8b03569
[9] Liu, Z. C. et al. Generative model for the inverse design of metasurfaces. Nano Lett. 18, 6570-6576 (2018). doi: 10.1021/acs.nanolett.8b03171
[10] Zhang, T. et al. Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks. Photonics Res. 7, 368-380 (2019). doi: 10.1364/PRJ.7.000368
[11] Aharon, N. et al. NV center based nano-NMR enhanced by deep learning. Prepint at arXiv: 1809.02583 (2018).
[12] Wiecha, P. R. et al. Pushing the limits of optical information storage using deep learning. Nat. Nanotechnol. 14, 237-244 (2019). doi: 10.1038/s41565-018-0346-1
[13] Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103-110 (2019). doi: 10.1038/s41592-018-0239-0
[14] Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3, 466-477 (2019). doi: 10.1038/s41551-019-0362-y
[15] Pilania, G. et al. Physics-informed machine learning for inorganic scintillator discovery. J. Chem. Phys. 148, 241729 (2018). doi: 10.1063/1.5025819
[16] Qiu, J. et al. Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm. Chem. Commun. 54, 7955-7958 (2018). doi: 10.1039/C8CC02850H
[17] Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73-76 (2016). doi: 10.1038/nature17439
[18] Oliynyk, A. O. & Mar, A. Discovery of intermetallic compounds from traditional to machine-learning approaches. ACC Chem. Res. 51, 59-68 (2018). doi: 10.1021/acs.accounts.7b00490
[19] Zhou, Q. et al. Learning atoms for materials discovery. Proc. Natl Acad. Sci. USA 115, E6411-E6417 (2018). doi: 10.1073/pnas.1801181115
[20] Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360-365 (2018). doi: 10.1126/science.aat2663
[21] Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437-1443 (2017). doi: 10.1364/OPTICA.4.001437
[22] Liu, T. R. et al. Deep learning-based super-resolution in coherent imaging systems. Sci. Rep. 9, 3926 (2019). doi: 10.1038/s41598-019-40554-1
[23] Ouyang, W. et al. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460-468 (2018). doi: 10.1038/nbt.4106
[24] Nehme, E. et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018). doi: 10.1364/OPTICA.5.000458
[25] Rivenson, Y. et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018). doi: 10.1038/lsa.2017.141
[26] Yao, R. Y. et al. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing-a deep learning approach. Light Sci. Appl. 8, 26 (2019). doi: 10.1038/s41377-019-0138-x
[27] Mannodi-Kanakkithodi, A. et al. Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond. Mater. Today 21, 785-796 (2018). doi: 10.1016/j.mattod.2017.11.021
[28] Asano, T. & Noda, S. Optimization of photonic crystal nanocavities based on deep learning. Opt. Express 26, 32704-32717 (2018). doi: 10.1364/OE.26.032704
[29] Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning. 1050-1059 (ICML, New York, 2016).
[30] Xin, D. et al. Accelerating human-in-the-loop machine learning: challenges and opportunities. In: Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning. (ACM, Houston, 2018).
[31] Lu, J. et al. Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. https://doi.org/10.1109/tkde.2018.2876857 (2018).