Recent advances in data-driven and artificial intelligence-integrated perovskite solar cells: From design to self-driving laboratories

Abstract

Perovskite solar cells (PSCs) have rapidly advanced owing to their excellent optoelectronic properties such as high absorption, long diffusion length, and high carrier mobility, achieving power conversion efficiencies of up to 27%. The ABX₃ crystal structure of perovskites and their various possible material combinations provide broad compositional and dimensional tunability, enabling tailored bandgaps, controlled stability, and targeted optoelectronic features. However, the efficiency of conventional trial-and-error approaches in discovering new materials is limited by the interplay between the compositional, material, and processing variables, highlighting the need for reproducible synthetic protocols and reliable datasets to support the high-throughput exploration of material combinations. Artificial intelligence (AI) technologies, including machine learning and large language models, leverage such datasets to provide predictive and generative capabilities for performance forecasting, compositional and process optimization, inverse design of novel materials, and literature knowledge extraction. Furthermore, the combination of an automated protocol setup, fabrication, high-throughput characterization using AI, and large device-level datasets has paved the way for building autonomous research platforms. Specifically, automation and robotics are integrated with in situ metrology and algorithmic guidance to reduce the build–measure–learn cycle from weeks to hours, thereby accelerating discovery and stability assessment. This review focuses on three central pillars of data-driven and AI research: data platforms, AI methodologies, and self-driving laboratories, which could collectively reshape PSC research into a systematic, autonomous, and scalable framework. By reviewing advances across these domains, we demonstrate how data-driven strategies can transform PSC development from intuition-based exploration to accelerated and reliable innovation, paving the way for practical deployment and commercialization.

Keywords

artificial intelligences / data platforms / material designs / perovskite solar cells / self-driving laboratories

References

[1] Park N-G. Perovskite solar cells: an emerging photovoltaic technology. Mater Today. 2015; 18(2): 65-72.

[2] Bai S, Wu Z, Wu X, et al. High-performance planar heterojunction perovskite solar cells: preserving long charge carrier diffusion lengths and interfacial engineering. Nano Res. 2014; 7(12): 1749-1758.

[3] Wang Y, Zhang Y, Zhang P, Zhang W. High intrinsic carrier mobility and photon absorption in the perovskite CH₃NH₃PbI₃. Phys Chem Chem Phys. 2015; 17(17): 11516-11520.

[4] Mahjabin S, Haque MM, Sobayel K, et al. Perceiving of defect tolerance in perovskite absorber layer for efficient perovskite solar cell. IEEE Access. 2020; 8: 106346-106353.

[5] Tang Y, Gu Z, Fu C, et al. FAPbI₃ perovskite solar cells: from film morphology regulation to device optimization. Solar RRL. 2022; 6(6):2200120.

[6] Jeon NJ, Noh JH, Kim YC, Yang WS, Ryu S, Seok SI. Solvent engineering for high-performance inorganic–organic hybrid perovskite solar cells. Nat Mater. 2014; 13(9): 897-903.

[7] Liu R, Xu K. Solvent engineering for perovskite solar cells: a review. Micro Nano Lett. 2020; 15(6): 349-353.

[8] Jeon NJ, Noh JH, Yang WS, et al. Compositional engineering of perovskite materials for high-performance solar cells. Nature. 2015; 517(7535): 476-480.

[9] Wang H, Zhang L, Cheng M, Ding L. Compositional engineering for lead halide perovskite solar cells. J Semicond. 2022; 43:080202.

[10] Cheng M, Zuo C, Wu Y, et al. Charge-transport layer engineering in perovskite solar cells. Sci Bull. 2020; 65(15): 1237-1241.

[11] Pan H, Zhao X, Gong X, et al. Advances in design engineering and merits of electron transporting layers in perovskite solar cells. Mater Horiz. 2020; 7(9): 2276-2291.

[12] Lee J, Son T, Min K, Park S, Kim Y, Seo J. Rationally designed hole transporting layer system for efficient and stable perovskite solar cells. EcoMat. 2023; 5(11):e12414.

[13] Chen J, Park N-G. Materials and methods for interface engineering toward stable and efficient perovskite solar cells. ACS Energy Lett. 2020; 5(8): 2742-2786.

[14] Bai Y, Meng X, Yang S. Interface engineering for highly efficient and stable planar p-i-n perovskite solar cells. Adv Energy Mater. 2018; 8:1701883.

[15] Zhang F, Zhu K. Additive engineering for efficient and stable perovskite solar cells. Adv Energy Mater. 2020; 10:1902579.

[16] NREL Best Research-Cell Efficiency Chart. https://www.nrel.gov/pv/assets/pdfs/bestresearch-cell-efficiencies.pdf. Accessed September 4, 2025.

[17] Qian J, Xu B, Tian W. A comprehensive theoretical study of halide perovskites ABX₃. Org Electron. 2016; 37: 61-73.

[18] Unger E, Kegelmann L, Suchan K, et al. Roadmap and roadblocks for the band gap tunability of metal halide perovskites. J Mater Chem A. 2017; 5(23): 11401-11409.

[19] Ren X, Yang Z, Yang D, et al. Modulating crystal grain size and optoelectronic properties of perovskite films for solar cells by reaction temperature. Nanoscale. 2016; 8(6): 3816-3822.

[20] Yusoff ARM, Nazeeruddin MK. Low-dimensional perovskites: from synthesis to stability in perovskite solar cells. Adv Energy Mater. 2018; 8:1702073.

[21] Li F, Liu M. Recent efficient strategies for improving the moisture stability of perovskite solar cells. J Mater Chem A. 2017; 5(30): 15447-15459.

[22] Aristidou N, Eames C, Sanchez-Molina I, et al. Fast oxygen diffusion and iodide defects mediate oxygen-induced degradation of perovskite solar cells. Nat Commun. 2017; 8(1):15218.

[23] Regalado-Pérez E, Díaz-Cruz EB, Landa-Bautista J, Mathews N, Mathew X. Impact of vertical inhomogeneity on the charge extraction in perovskite solar cells: a study by depth-dependent photoluminescence. ACS Appl Mater Interfaces. 2021; 13(10): 11833-11844.

[24] Park H, Lee DG, Kim Y, et al. Overcoming strain-induced vertical inhomogeneity in perovskite films for all-perovskite tandem solar cells. InfoMat. 2025; 8(1):e70091.

[25] Jung HJ, Kim D, Kim S, et al. Stability of halide perovskite solar cell devices: in situ observation of oxygen diffusion under biasing. Adv Mater. 2018; 30:1802769.

[26] Divitini G, Cacovich S, Matteocci F, et al. In situ observation of heat-induced degradation of perovskite solar cells. Nat Energy. 2016; 1(2): 1-6.

[27] Tsai H, Asadpour R, Blancon J-C, et al. Light-induced lattice expansion leads to high-efficiency perovskite solar cells. Science. 2018; 360(6384): 67-70.

[28] Li Z, Xiao C, Yang Y, et al. Extrinsic ion migration in perovskite solar cells. Energy Environ Sci. 2017; 10(5): 1234-1242.

[29] Gualdrón-Reyes AF, Yoon SJ, Mora-Sero I. Recent insights for achieving mixed halide perovskites without halide segregation. Curr Opin Electrochem. 2018; 11: 84-90.

[30] Yang B, Bogachuk D, Suo J, et al. Strain effects on halide perovskite solar cells. Chem Soc Rev. 2022; 51(17): 7509-7530.

[31] Gu X, Xiang W, Tian Q, Liu S. Rational surface-defect control via designed passivation for high-efficiency inorganic perovskite solar cells. Angew Chem Int Ed Engl. 2021; 60(43): 23164-23170.

[32] Gao ZW, Wang Y, Choy WC. Buried interface modification in perovskite solar cells: a materials perspective. Adv Energy Mater. 2022; 12:2104030.

[33] Cheng W, Huang P, Gao Z, et al. Molecular bridging of buried Interface flattens grain boundary grooves and imparts stress relaxation for performance enhancement and UV stability in perovskite solar cells. Adv Energy Mater. 2025; 15(30):2501296.

[34] Wang M, Shi Z, Fei C, et al. Ammonium cations with high pKa in perovskite solar cells for improved high-temperature photostability. Nat Energy. 2023; 8(11): 1229-1239.

[35] Baumann S, Eperon GE, Virtuani A, et al. Stability and reliability of perovskite containing solar cells and modules: degradation mechanisms and mitigation strategies. Energy Environ Sci. 2024; 17(20): 7566-7599.

[36] Yang Y, Peng H, Liu C, et al. Bi-functional additive engineering for high-performance perovskite solar cells with reduced trap density. J Mater Chem A. 2019; 7(11): 6450-6458.

[37] Grancini G, Roldán-Carmona C, Zimmermann I, et al. One-year stable perovskite solar cells by 2D/3D interface engineering. Nat Commun. 2017; 8:15684.

[38] Liu C, Cheng Y-B, Ge Z. Understanding of perovskite crystal growth and film formation in scalable deposition processes. Chem Soc Rev. 2020; 49(6): 1653-1687.

[39] Rong Y, Hu Y, Mei A, et al. Challenges for commercializing perovskite solar cells. Science. 2018; 361(6408): eaat8235.

[40] Cheng Y, So F, Tsang S-W. Progress in air-processed perovskite solar cells: from crystallization to photovoltaic performance. Mater Horiz. 2019; 6(8): 1611-1624.

[41] Saliba M, Correa-Baena J-P, Wolff CM, et al. How to make over 20% efficient perovskite solar cells in regular (n–i–p) and inverted (p–i–n) architectures. Chem Mater. 2018; 30(13): 4193-4201.

[42] Wang S, Sun J, Xue J, Wang R. Protocol for fabricating long-lasting passivated perovskite solar cells. STAR Protoc. 2024; 5:103265.

[43] Baumann DO, Laufer F, Roger J, et al. Repeatable perovskite solar cells through fully automated spin-coating and quenching. ACS Appl Mater Interfaces. 2024; 16(40): 54007-54016.

[44] Zhang J, Liu B, Liu Z, et al. Optimizing perovskite thin-film parameter spaces with machine learning-guided robotic platform for high-performance perovskite solar cells. Adv Energy Mater. 2023; 13:2302594.

[45] Eguchi N, Fukazawa T, Kanda H, Yamamoto K, Miyake T, Murakami TN. Performance optimization of perovskite solar cells with an automated spin coating system and artificial intelligence technologies. EES Solar. 2025; 1(3): 320-330.

[46] Liu Y, Yang Z, Yu Z, et al. Generative artificial intelligence and its applications in materials science: current situation and future perspectives. J Mater. 2023; 9(4): 798-816.

[47] Bhardwaj A, Kishore S, Pandey DK. Artificial intelligence in biological sciences. Life. 2022; 12(9):1430.

[48] Malek II, Imtiaz H, Subrina S. Simultaneous optimization of efficiency and degradation in tunable HTL-free perovskite solar cells with MWCNT-integrated back contact using a machine learning-derived polynomial regressor. arXiv preprint arXiv:2505.18693. 2025.

[49] Liu X, Sun P, Chen S, et al. Perovskite-llm: knowledge-enhanced large language models for perovskite solar cell research. arXiv preprint arXiv:2502.12669. 2025.

[50] Zhang J, Wu J, Stroyuk O, et al. Self-driving AMADAP laboratory: accelerating the discovery and optimization of emerging perovskite photovoltaics. MRS Bull. 2024; 49(12): 1284-1294.

[51] Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 2013; 1(1):011002.

[52] Zagorac D, Muller H, Ruehl S, Zagorac J, Rehme S. Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features. J Appl Cryst. 2019; 52(5): 918-925.

[53] Kim S, Chen J, Cheng T, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021; 49(D1): D1388-D1395.

[54] Chenebuah ET, Nganbe M, Tchagang AB. A Fourier-transformed feature engineering design for predicting ternary perovskite properties by coupling a two-dimensional convolutional neural network with a support vector machine (Conv2D-SVM). Mater Res Express. 2023; 10(2):026301.

[55] Rath S, Sudha Priyanga G, Nagappan N, Thomas T. Discovery of direct band gap perovskites for light harvesting by using machine learning. Comput Mater Sci. 2022; 210:111476.

[56] Wang Z, Han Y, Lin X, Cai J, Wu S, Li J. An ensemble learning platform for the large-scale exploration of new double perovskites. ACS Appl Mater Interfaces. 2022; 14(1): 717-725.

[57] Chen L, Wang X, Xia W, Liu C. PSO-SVR predicting for the Ehull of ABO₃-type compounds to screen the thermodynamic stable perovskite candidates based on multi-scale descriptors. Comput Mater Sci. 2022; 211:111435.

[58] Li L, Tao Q, Xu P, Yang X, Lu W, Li MJ. Studies on the regularity of perovskite formation via machine learning. Comput Mater Sci. 2021; 199:110712.

[59] Jacobsson TJ, Hultqvist A, García-Fernández A, et al. An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nat Energy. 2022; 7(1): 107-115.

[60] NOMAD Lab. NOMAD solar cells database. https://nomad-lab.eu/prod/v1/staging/gui/search/solarcells. Accessed September 4, 2025.

[61] Ab Initio Materials Simulations Group. HybriD³ database. https://materials.hybrid3.duke.edu/. Accessed September 4, 2025.

[62] Lee Y-L, Im J, Chang H, et al. Chemdx perovskite solar cell data platform. https://solar.chemdx.org/. Accessed September 4, 2025.

[63] Swain MC, Cole JM. ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J Chem Inf Model. 2016; 56(10): 1894-1904.

[64] Beard EJ, Cole JM. Perovskite- and dye-sensitized solar-cell device databases auto-generated using ChemDataExtractor. Sci Data. 2022; 9(1):329.

[65] Xie T, Wan Y, Zhou Y, et al. Creation of a structured solar cell material dataset and performance prediction using large language models. Patterns. 2024; 5(5):100955.

[66] Raccuglia P, Elbert KC, Adler PDF, et al. Machine-learning-assisted materials discovery using failed experiments. Nature. 2016; 533(7601): 73-76.

[67] Toniato A, Vaucher AC, Laino T, Graziani M. Negative chemical data boosts language models in reaction outcome prediction. Sci Adv. 2025; 11(24):eadt5578.

[68] Zhao Y, Zhang J, Xu Z, et al. Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning. Nat Commun. 2021; 12(1):2191.

[69] Gu E, Tang X, Langner S, et al. Robot-based high-throughput screening of antisolvents for lead halide perovskites. Joule. 2020; 4(8): 1806-1822.

[70] Chen S, Hou Y, Chen H, et al. Exploring the stability of novel wide bandgap perovskites by a robot based high throughput approach. Adv Energy Mater. 2018; 8:1701543.

[71] Xu J, Moran CH, Ghorai A, et al. Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals. Nat Commun. 2025; 16:7841.

[72] Liu S, Nie W, Wang C, et al. Multi-modal molecule structure–text model for text-based retrieval and editing. Nat Mach Intell. 2023; 5(12): 1447-1457.

[73] Huang H, Magar R, Barati Farimani A. Pretraining strategies for structure agnostic material property prediction. J Chem Inf Model. 2024; 64(3): 627-637.

[74] Pu Y, Dai Z, Zhou Y, et al. Data-driven molecular encoding for efficient screening of organic additives in perovskite solar cells. Adv Funct Mater. 2026; 36(1):e06672.

[75] Zheng Z, Zhang O, Borgs C, Chayes JT, Yaghi OM. ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis. J Am Chem Soc. 2023; 145(32): 18048-18062.

[76] Zheng Z, Alawadhi AH, Chheda S, et al. Shaping the water-harvesting behavior of metal–organic frameworks aided by fine-tuned GPT models. J Am Chem Soc. 2023; 145(51): 28284-28295.

[77] Bran M, Cox S, Schilter O, et al. Augmenting large language models with chemistry tools. Nat Mach Intell. 2024; 6(5): 525-535.

[78] Zhang X, Ni Z, Fanady B, et al. ChatGPT-assisted rational design for iterative performance optimization of perovskite solar cells. Accessed January, 2025.

[79] Wang F, Zhai Z, Zhao Z, Di Y, Chen X. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun. 2024; 15:4332.

[80] Zhao W, Chen Z, Shang Y, et al. A physics-informed machine learning framework for accelerated discovery of single-phase B2 multi-principal element intermetallics. NPJ Comput Mater. 2025; 11(1):278.

[81] Wang S-H, Pillai HS, Wang S, Achenie LEK, Xin H. Infusing theory into deep learning for interpretable reactivity prediction. Nat Commun. 2021; 12(1):5288.

[82] Mukherjee A, Zavala VM. Physics-constrained machine learning for chemical engineering. arXiv preprint arXiv:2508.20649. 2025.

[83] Guo K, Yang Z, Yu C-H, Buehler MJ. Artificial intelligence and machine learning in design of mechanical materials. Mater Horiz. 2021; 8(4): 1153-1172.

[84] Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021; 6(8): 655-678.

[85] Lu Y, Wei D, Liu W, et al. Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results. J Energy Chem. 2023; 77: 200-208.

[86] Lee S, Park YJ, Kim J, et al. Data-driven analysis on perovskite solar cell devices. Curr Appl Phys. 2024; 68: 98-107.

[87] Li J, Pradhan B, Gaur S, Thomas J. Predictions and strategies learned from machine learning to develop high-performing perovskite solar cells. Adv Energy Mater. 2019; 9:1901891.

[88] Dunlap-Shohl WA, Meng Y, Sunkari PP, et al. Physiochemical machine learning models predict operational lifetimes of CH₃NH₃PbI₃ perovskite solar cells. J Mater Chem A. 2024; 12(16): 9730-9746.

[89] Zhao S, Zhou S, Guo Z, et al. Machine learning-assisted analysis of perovskite solar cell long-term stability under multiple environmental factors. ACS Sustain Chem Eng. 2025; 13(19): 7155-7165.

[90] Yang A, Sun Y, Zhang J, et al. Enhancing power conversion efficiency of perovskite solar cells through machine learning guided experimental strategies. Adv Funct Mater. 2025; 35:2410419.

[91] Zhang R, Motes B, Tan S, et al. Machine learning prediction of organic–inorganic halide perovskite solar cell performance from optical properties. ACS Energy Lett. 2025; 10(4): 1714-1724.

[92] Liu Z, Zhang J, Rao G, et al. Accelerating photostability evaluation of perovskite films through intelligent spectral learning-based early diagnosis. ACS Energy Lett. 2024; 9(2): 662-670.

[93] Kouroudis I, Tanko KT, Karimipour M, et al. Artificial intelligence-based, wavelet-aided prediction of long-term outdoor performance of perovskite solar cells. ACS Energy Lett. 2024; 9(4): 1581-1586.

[94] Mao L, Xiang C. A comprehensive review of machine learning applications in perovskite solar cells: materials discovery, device performance, process optimization and systems integration. Mater Today Energy. 2025; 47:101742.

[95] Liu Y, Tan X, Liang J, Han H, Xiang P, Yan W. Machine learning for perovskite solar cells and component materials: key technologies and prospects. Adv Funct Mater. 2023; 33(17):2214271.

[96] de la Asunción-Nadal V, Iliffe Sprague C, Guijarro-Berdiñas B, Cappel UB, García-Fernández A. Machine learning for perovskite solar cells: a comprehensive review on opportunities and challenges for materials scientists. EES Solar. 2025; 1(6): 927-957.

[97] Lee S-W, Lee S, Lee S, et al. Bridging artificial intelligence with photovoltaics. Cell Rep Phys Sci. 2025; 6(11):102893.

[98] Liu Z, Rolston N, Flick AC, et al. Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing. Joule. 2022; 6(4): 834-849.

[99] Laufer F, Götz M, Paetzold UW. Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication. Energy Environ Sci. 2025; 18(4): 1767-1782.

[100] Sun S, Tiihonen A, Oviedo F, et al. A data fusion approach to optimize compositional stability of halide perovskites. Matter. 2021; 4(4): 1305-1322.

[101] Ali A, Park H, Mall R, et al. Machine learning accelerated recovery of the cubic structure in mixed-cation perovskite thin films. Chem Mater. 2020; 32(7): 2998-3006.

[102] Zhang B, Zeng H, Yin H, et al. Combining component screening, machine learning and molecular engineering for the design of high-performance inverted perovskite solar cells. Energy Environ Sci. 2024; 17(15): 5532-5541.

[103] Hu Y, Hu X, Zhang L, et al. Machine-learning modeling for ultra-stable high-efficiency perovskite solar cells. Adv Energy Mater. 2022; 12(41):2201463.

[104] Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter engineering. Science. 2018; 361(6400): 360-365.

[105] Noh J, Gu GH, Kim S, Jung Y. Machine-enabled inverse design of inorganic solid materials: promises and challenges. Chem Sci. 2020; 11(19): 4871-4881.

[106] Liu W, Meng N, Huo X, 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. 2023; 83: 128-137.

[107] Yan G, Tang H, Shen Y, Han L, Han Q. AI-generated ammonium ligands for high-efficiency and stable 2D/3D heterojunction perovskite solar cells. Adv Mater. 2025; 37:2503154.

[108] Xu J, Chen H, Grater L, et al. Anion optimization for bifunctional surface passivation in perovskite solar cells. Nat Mater. 2023; 22(12): 1507-1514.

[109] Chen C, Maqsood A, Zhang Z, et al. The use of ChatGPT to generate experimentally testable hypotheses for improving the surface passivation of perovskite solar cells. Cell Rep Phys Sci. 2024; 5:102058.

[110] Zhu Z, Mao K, Zhang K, et al. Correlating the perovskite/polymer multi-mode reactions with deep-level traps in perovskite solar cells. Joule. 2022; 6(12): 2849-2868.

[111] Marshall J, Sanchez SL, Desai R, et al. POLARIS: perovskite optimization using LLM-assisted refinement and intelligent screening. ChemRxiv. Accessed June 10, 2025.

[112] Wu J, Torresi L, Hu M, et al. Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells. Science. 2024; 386(6727): 1256-1264.

[113] Zhang Y, Zhou Y. Machine learning quantification of grain characteristics for perovskite solar cells. Matter. 2024; 7(1): 255-265.

[114] Laufer F, Ziegler S, Schackmar F, et al. Process insights into perovskite thin-film photovoltaics from machine learning with in situ luminescence data. Solar RRL. 2023; 7(7):2201114.

[115] Liu Y, Yang J, Vasudevan RK, et al. Exploring the relationship of microstructure and conductivity in metal halide perovskites via active learning-driven automated scanning probe microscopy. J Phys Chem Lett. 2023; 14(13): 3352-3359.

[116] Zhao Y, Heumueller T, Zhang J, et al. A bilayer conducting polymer structure for planar perovskite solar cells with over 1,400 hours operational stability at elevated temperatures. Nat Energy. 2022; 7(2): 144-152.

[117] Lei B, Svensson PH, Yushmanov P, Kloo L. AURORA—an automatic robotic platform for materials discovery. ACS Appl Mater Interfaces. 2025; 17(18): 26701-26709.

[118] Gunasekaran RK, Jung J, Yang SW, et al. High-throughput compositional mapping of triple-cation tin–lead perovskites for high-efficiency solar cells. InfoMat. 2023; 5(4):e12393.

[119] Deng C, Tang L, Luo P, et al. Unveiling the statistical behaviors of metal-halide perovskites from films to devices through a high-throughput experimental platform. InfoMat. 2026; 8(1):e70039.

[120] Tom G, Schmid SP, Baird SG, et al. Self-driving laboratories for chemistry and materials science. Chem Rev. 2024; 124(16): 9633-9732.

[121] Cakan DN, Oberholtz E, Kaushal K, Dunfield SP, Fenning DP. Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors. Mater Adv. 2025; 6(2): 598-606.

[122] MacLeod BP, Parlane FGL, Morrissey TD, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020; 6(20):eaaz8867.

[123] Roch LM, Häse F, Kreisbeck C, et al. ChemOS: orchestrating autonomous experimentation. Sci Robot. 2018; 3(19):eaat5559.

[124] Meftahi N, Surmiak MA, Fürer SO, et al. Machine learning enhanced high-throughput fabrication and optimization of quasi-2D Ruddlesden–Popper perovskite solar cells. Adv Energy Mater. 2023; 13:2203859.

[125] Wu X, Chen H, Wang J, Niu X. Machine learning accelerated study of defect energy levels in perovskites. J Phys Chem C. 2023; 127(23): 11387-11395.

[126] Tao Q, Xu P, Li M, Lu W. Machine learning for perovskite materials design and discovery. NPJ Comput Mater. 2021; 7(1):23.

[127] Machine-learning-accelerated selection of perovskite passivants. Nat Mater. 2023; 22(12): 1449-1450.

[128] Rahman W, Zhong C, Liu H, et al. Enhancing perovskite solar cell efficiency and stability: a multimodal prediction approach integrating microstructure, composition, and processing technology. Nanoscale. 2025; 17(26): 15935-15949.

[129] Shao S, Loi MA. The role of the interfaces in perovskite solar cells. Adv Mater Interfaces. 2020; 7:1901469.

[130] Ward L, Babinec S, Dufek EJ, et al. Principles of the battery data genome. Joule. 2022; 6(10): 2253-2271.

[131] JARVIS (Joint Automated Repository for Various Integrated Simulations). JARVIS. https://jarvis.nist.gov/. Accessed October 16, 2025.

[132] Näsström H, Márquez JA, Ginzburg L. NOMAD Perovskite Solar Cells Database (v1.2.7). Zenodo; 2025.

[133] Khenkin MV, Katz EA, Abate A, et al. Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures. Nat Energy. 2020; 5(1): 35-49.

[134] Dunbar RB, Duck BC, Moriarty T, et al. How reliable are efficiency measurements of perovskite solar cells? The first inter-comparison, between two accredited and eight non-accredited laboratories. J Mater Chem A. 2017; 5(43): 22542-22558.

[135] Porrès L, Holland A, Pålsson L-O, Monkman AP, Kemp C, Beeby A. Absolute measurements of photoluminescence quantum yields of solutions using an integrating sphere. J Fluoresc. 2006; 16(2): 267-273.

[136] De Rossi F, Barbé J, Tanenbaum DM, et al. An interlaboratory study on the stability of all-printable hole transport material–free perovskite solar cells. Energ Technol. 2020; 8(12):2000134.

[137] Chen C-Y, Chang Y-F, Shih Y-C, et al. Round-Robin interlaboratory comparison of large-area organic thin-film and perovskite solar cells. Sol RRL. 2025; 9(22):e202500538.

[138] Hidenori S, Masahiro Y, Hiromi T, et al. Round-Robin inter-comparison of maximum power measurement for metastable perovskite solar cells. ECS J Solid State Sci Technol. 2022; 11(5):055008.

[139] Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018; 559(7715): 547-555.

[140] Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019; 571(7763): 95-98.

[141] Gupta T, Zaki M, Krishnan NMA, Mausam . MatSciBERT: a materials domain language model for text mining and information extraction. NPJ Comput Mater. 2022; 8:102.

[142] Du X, Lüer L, Heumueller T, et al. Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning. InfoMat. 2024; 6(7):e12554.

RIGHTS & PERMISSIONS

2026 The Author(s). InfoMat published by UESTC and John Wiley & Sons Australia, Ltd.