[1] G. Xing et al. Long-range balanced electron-and hole-transport lengths in organic-inorganic CH3NH3PbI3. Science, 342, 344-347(2013). https://doi.org/10.1126/science.1243167

[2] H. Tsai et al. High-efficiency two-dimensional Ruddlesden–Popper perovskite solar cells. Nature, 536, 312-316(2016).

[3] N. T. P. Hartono et al. The effect of structural dimensionality on carrier mobility in lead-halide perovskites. J. Mater. Chem. A, 7, 23949-23957(2019).

[4] S. D. Stranks et al. Electron-hole diffusion lengths exceeding 1 micrometer in an organometal trihalide perovskite absorber. Science, 342, 341-344(2013).

[5] M. Liu, M. B. Johnston, H. J. Snaith. Efficient planar heterojunction perovskite solar cells by vapour deposition. Nature, 501, 395-398(2013).

[6] M. M. Lee et al. Efficient hybrid solar cells based on meso-superstructured organometal halide perovskites. Science, 338, 643-647(2012).

[7] H.-S. Kim et al. Lead iodide perovskite sensitized all-solid-state submicron thin film mesoscopic solar cell with efficiency exceeding 9%. Sci. Rep., 2, 591(2012).

[8] Z.-K. Tan et al. Bright light-emitting diodes based on organometal halide perovskite. Nat. Nanotechnol., 9, 687-692(2014).

[9] L. C. Schmidt et al. Nontemplate synthesis of CH3NH3PbBr3 perovskite nanoparticles. J. Am. Chem. Soc., 136, 850-853(2014). https://doi.org/10.1021/ja4109209

[10] C. Motta et al. Revealing the role of organic cations in hybrid halide perovskite CH3NH3PbI3. Nat. Commun., 6, 7026(2015). https://doi.org/10.1038/ncomms8026

[11] Z. Huang et al. Anion–π interactions suppress phase impurities in FAPbI3 solar cells. Nature, 623, 531-537(2023). https://doi.org/10.1038/s41586-023-06637-w

[12] M. A. Green et al. Solar cell efficiency tables (version 63). Prog. Photovoltaics Res. Appl., 32, 3-13(2024).

[13] H. Chen et al. Improved charge extraction in inverted perovskite solar cells with dual-site-binding ligands. Science, 384, 189-193(2024).

[14] J. Zhou et al. Highly efficient and stable perovskite solar cells via a multifunctional hole transporting material. Joule, 8, 1691-1706(2024).

[15] M. Li et al. Acceleration of radiative recombination for efficient perovskite LEDs. Nature, 630, 631-635(2024).

[16] Y. Cao et al. Perovskite light-emitting diodes based on spontaneously formed submicrometre-scale structures. Nature, 562, 249-253(2018).

[17] Y. He et al. CsPbBr3 perovskite detectors with 1.4% energy resolution for high-energy γ-rays. Nat. Photonics, 15, 36-42(2021). https://doi.org/10.1038/s41566-020-00727-1

[18] J. Jiang et al. Red perovskite light-emitting diodes with efficiency exceeding 25% realized by co-spacer cations. Adv. Mater., 34, 2204460(2022).

[19] J. S. Kim et al. Ultra-bright, efficient and stable perovskite light-emitting diodes. Nature, 611, 688-694(2022).

[20] Y. Jiang et al. Synthesis-on-substrate of quantum dot solids. Nature, 612, 679-684(2022).

[21] Y. Sun et al. Bright and stable perovskite light-emitting diodes in the near-infrared range. Nature, 615, 830-835(2023).

[22] W. Bai et al. Perovskite light-emitting diodes with an external quantum efficiency exceeding 30%. Adv. Mater., 35, 2302283(2023).

[23] S. Yuan et al. Efficient blue electroluminescence from reduced-dimensional perovskites. Nat. Photonics, 18, 425-431(2024).

[24] L. Kong et al. Fabrication of red-emitting perovskite LEDs by stabilizing their octahedral structure. Nature, 631, 73-79(2024).

[25] Y. Feng et al. Nucleophilic reaction-enabled chloride modification on CsPbI3 quantum dots for pure red light-emitting diodes with efficiency exceeding 26%. Angew. Chem. Int. Ed., 63, e202318777(2024). https://doi.org/10.1002/anie.202318777

[26] J. Jiang et al. Efficient pure-red perovskite light-emitting diodes with strong passivation via ultrasmall-sized molecules. Sci. Adv., 10, eadn5683(2024).

[27] H. Zhu et al. Lead halide perovskite nanowire lasers with low lasing thresholds and high quality factors. Nat. Mater., 14, 636-642(2015).

[28] C. Bao et al. Bidirectional optical signal transmission between two identical devices using perovskite diodes. Nat. Electron., 3, 156-164(2020).

[29] K. Sakhatskyi et al. Stable perovskite single-crystal X-ray imaging detectors with single-photon sensitivity. Nat. Photonics, 17, 510-517(2023).

[30] K. Wen et al. Continuous-wave lasing in perovskite LEDs with an integrated distributed feedback resonator. Adv. Mater., 35, 2303144(2023).

[31] L. Gu et al. High Q-factor and low threshold continuous-wave near-infrared lasing with quasi-2D perovskites. Adv. Funct. Mater., 33, 2303900(2023).

[32] C. Qin et al. Stable room-temperature continuous-wave lasing in quasi-2D perovskite films. Nature, 585, 53-57(2020).

[33] Q. Xu et al. Rationalizing perovskite data for machine learning and materials design. J. Phys. Chem. Lett., 9, 6948-6954(2018).

[34] N. Wang et al. Perovskite light-emitting diodes based on solution-processed self-organized multiple quantum wells. Nat. Photonics, 10, 699-704(2016).

[35] L. Zhu et al. Large organic cations in quasi-2D perovskites for high-performance light-emitting diodes. J. Phys. Chem. Lett., 11, 8502-8510(2020).

[36] V. Gladkikh et al. Machine learning for predicting the band gaps of ABX3 perovskites from elemental properties. J. Phys. Chem. C, 124, 8905-8918(2020). https://doi.org/10.1021/acs.jpcc.9b11768

[37] K. Higgins et al. Chemical robotics enabled exploration of stability in multicomponent lead halide perovskites via machine learning. ACS Energy Lett., 5, 3426-3436(2020).

[38] J. Kirman et al. Machine-learning-accelerated perovskite crystallization. Matter, 2, 938-947(2020).

[39] L. Zhang et al. Deep learning for additive screening in perovskite light‐emitting diodes. Angew. Chem. Int. Ed., 61, e202209337(2022).

[40] L. Zhang et al. Prediction of operational lifetime of perovskite light emitting diodes by machine learning. Adv. Intell. Syst., 6, 2300772(2024).

[41] N. K. Bansal et al. Machine learning in perovskite solar cells: recent developments and future perspectives. Energy Tech., 11, 2300735(2023).

[42] Y. Liu et al. Machine learning for perovskite solar cells and component materials: key technologies and prospects. Adv. Funct. Mater., 33, 2214271(2023).

[43] T. Liu et al. Machine-learning accelerating the development of perovskite photovoltaics. Sol. RRL, 7, 2300650(2023).

[44] T. J. Jacobsson et al. An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nat. Energy, 7, 107-115(2022).

[45] Q. Tao et al. Machine learning for perovskite materials design and discovery. NPJ Comput. Mater., 7, 23(2021).

[46] M. Srivastava et al. Machine learning roadmap for perovskite photovoltaics. J. Phys. Chem. Lett., 12, 7866-7877(2021).

[47] J. Benesty, J. Benesty, W. Kellermann et al. Pearson correlation coefficient. Noise Reduction in Speech Processing, 2, 1-4(2009).

[48] N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9, 62-66(1979).

[49] L. Breiman. Random forests. Mach. Learn., 45, 5-32(2001).

[50] C. Cortes, V. Vapnik. Support-vector networks. Mach. Learn., 20, 273-297(1995).

[51] S. Albawi, T. A. Mohammed, S. Al-Zawi. Understanding of a convolutional neural network, 1-6(2017).

[52] S. M. Lundberg, S.-I. Lee. A unified approach to interpreting model predictions(2017).

[53] R. Caruana, A. Niculescu-Mizil. An empirical comparison of supervised learning algorithms, 161-168(2006).

[54] J. K. Mandal, P. C. Sen, M. Hajra, M. Ghosh. Supervised classification algorithms in machine learning: a survey and review. Emerging Technology in Modelling and Graphics, 99-111(2020).

[55] M. Cord, P. Cunningham, M. Cord, S. J. Delany. Supervised learning. Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval, 21-49(2008).

[56] T. Hastie, J. Friedman, R. Tibshirani. The Elements of Statistical Learning(2001).

[57] S. B. Kotsiantis, I. D. Zaharakis, P. E. Pintelas. Machine learning: a review of classification and combining techniques. Artif. Intell. Rev., 26, 159-190(2006).

[58] I. G. Maglogiannis. Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in EHealth, HCI, Information Retrieval and Pervasive Technologies(2007).

[59] A. E. Maxwell, T. A. Warner, F. Fang. Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens., 39, 2784-2817(2018).

[60] K. P. Murphy. Machine Learning: A Probabilistic Perspective(2012).

[61] V. Stanev et al. Machine learning modeling of superconducting critical temperature. NPJ Comput. Mater., 4, 29(2018).

[62] A. J. Myles et al. An introduction to decision tree modeling. J. Chemom., 18, 275-285(2004).

[63] J. H. Friedman. Stochastic gradient boosting. Comput. Stat. Data Anal., 38, 367-378(2002).

[64] T. Hastie, T. Hastie, R. Tibshirani, J. Friedman. Unsupervised learning. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 485-585(2009).

[65] G. Hinton, T. J. Sejnowski. Unsupervised Learning: Foundations of Neural Computation(1999).

[66] Z. Ghahramani. Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures(2004).

[67] M. Cord, D. Greene, P. Cunningham, R. Mayer. Unsupervised learning and clustering. Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval, 51-90(2008).

[68] K. R. Chowdhary, K. R. Chowdhary. Natural language processing. Fundamentals of Artificial Intelligence, 603-649(2020).

[69] A. Gardin et al. Classifying soft self-assembled materials via unsupervised machine learning of defects. Commun. Chem., 5, 82(2022).

[70] D. A. Reynolds. Gaussian mixture models. Encyclopedia Biometr., 741, 659-663(2009).

[71] T. Kohonen. The self-organizing map. Proc. IEEE, 78, 1464-1480(1990).

[72] X. Zhu, A. B. Goldberg. Introduction to Semi-Supervised Learning(2022).

[73] X. Zhai et al. S4L: self-supervised semi-supervised learning, 1476-1485(2019).

[74] O. Chapelle, B. Scholkopf, A. Zien. Semi-supervised learning (Chapelle, O. et al., Ed.; 2006) [Book reviews]. IEEE Trans. Neural Netw., 20, 542(2009).

[75] X. Zhu. Semi-Supervised Learning Literature Survey(2005).

[76] M.-R. Amini et al. Self-training: a survey(2023).

[77] M.-F. Balcan, A. Blum, K. Yang. Co-training and expansion: towards bridging theory and practice, 89-96(2004).

[78] N. T. P. Hartono et al. How machine learning can help select capping layers to suppress perovskite degradation. Nat. Commun., 11, 4172(2020).

[79] C. Zhi et al. Machine-learning-assisted screening of interface passivation materials for perovskite solar cells. ACS Energy Lett., 8, 1424-1433(2023).

[80] C. She et al. Machine learning-guided search for high-efficiency perovskite solar cells with doped electron transport layers. J. Mater. Chem. A, 9, 25168-25177(2021).

[81] W. Liu et al. Machine learning enables intelligent screening of interface materials towards minimizing voltage losses for p-i-n type perovskite solar cells. J. Energy Chem., 83, 128-137(2023).

[82] S. Kim et al. PubChem substance and compound databases. Nucl. Acids Res., 44, D1202-D1213(2016).

[83] Y. Hu et al. Machine‐learning modeling for ultra‐stable high‐efficiency perovskite solar cells. Adv. Energy Mater., 12, 2201463(2022).

[84] T. Bak et al. Accelerated design of high-efficiency lead-free tin perovskite solar cells via machine learning. Int. J. Precis. Eng. Manuf. Green Technol., 10, 109-121(2023).

[85] J. Wang et al. Advancing vapor-deposited perovskite solar cells via machine learning. J. Mater. Chem. A, 11, 13201-13208(2023).

[86] N. Taherimakhsousi et al. A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics. NPJ Comput. Mater., 7, 190(2021).

[87] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition(2015).

[88] S. Wang et al. Carrier dynamics determines the optimization strategies of perovskite LEDs and PVs. Research, 6, 0112(2023).

[89] F. Scarselli et al. The graph neural network model. IEEE Trans. Neural Netw., 20, 61-80(2009).