Accelerating Perovskite Solar Cell Development Through High-Throughput Technologies: Computational Simulation, Automated Experimentation, and Intelligent Integration

Abstract

Perovskite solar cells have emerged as promising candidates for next-generation photovoltaics. However, optimizing their efficiency and long-term stability is complicated by the vast parameter space involving composition, processing, and device architecture, and so on. Conventional trial-and-error approaches are labor-intensive and often inefficient for navigating these complex multidimensional interactions. Consequently, the field is shifting toward high-throughput (HT) data-driven methodologies capable of accelerating the development cycle. This review highlights recent progress in HT computational and experimental strategies, along with their synergistic integration. Regarding computational efforts, we summarize the use of density functional theory and machine learning to identify single, double, and derivative perovskite candidates, as well as emerging techniques in literature data mining. We also discuss automated experimental platforms utilizing automatic processing and combinatorial characterization to optimize material compositions, device fabrication processes, and operational stability. Furthermore, we examine the integration of these domains through closed-loop workflows that combine computational prediction with automated experimentation to establish intelligent prediction, fabrication, and validation cycles. Finally, we provide an outlook on prevailing challenges regarding data acquisition, machine learning integration, HT platform infrastructure, and multi-modal data processing, alongside potential solutions for achieving fully autonomous material discovery and device optimization.

Keywords

automated experimentation / high-throughput computation / machine learning / perovskite solar cell

References

[1] A. Kojima, K. Teshima, Y. Shirai, and T. Miyasaka, “Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells,” Journal of the American Chemical Society 131, no. 17 (2009): 6050–6051, https://doi.org/10.1021/ja809598r.

[2] H. Chen, C. Liu, J. Xu, et al., “Improved Charge Extraction in Inverted Perovskite Solar Cells With Dual-Site-Binding Ligands,” Science 384, no. 6692 (2024): 189–193, https://doi.org/10.1126/science.adm9474.

[3] National Renewable Energy Laboratory (NREL), “Best Research-Cell Efficiency Chart,” accessed 2025, https://www.nrel.gov/pv/interactive-cell-efficiency.

[4] N. J. Jeon, J. H. Noh, Y. C. Kim, W. S. Yang, S. Ryu, and S. I. Seok, “Solvent Engineering for High-Performance Inorganic–Organic Hybrid Perovskite Solar Cells,” Nature Materials 13, no. 9 (2014): 897–903, https://doi.org/10.1038/nmat4014.

[5] N. J. Jeon, J. H. Noh, W. S. Yang, et al., “Compositional Engineering of Perovskite Materials for High-Performance Solar Cells,” Nature 517, no. 7535 (2015): 476–480, https://doi.org/10.1038/nature14133.

[6] S. Tang, Y. Deng, X. Zheng, et al., “Composition Engineering in Doctor-Blading of Perovskite Solar Cells,” Advanced Energy Materials 7, no. 18 (2017): 1700302, https://doi.org/10.1002/aenm.201700302.

[7] A. Polman, M. Knight, E. C. Garnett, B. Ehrler, and W. C. Sinke, “Photovoltaic Materials: Present Efficiencies and Future Challenges,” Science 352, no. 6283 (2016): aad4424, https://doi.org/10.1126/science.aad4424.

[8] Y. Rong, Y. Hu, A. Mei, et al., “Challenges for Commercializing Perovskite Solar Cells,” Science 361, no. 6408 (2018): eaat8235, https://doi.org/10.1126/science.aat8235.

[9] C. J. Bartel, J. M. Clary, C. Sutton, et al., “Inorganic Halide Double Perovskites With Optoelectronic Properties Modulated by Sublattice Mixing,” Journal of the American Chemical Society 142, no. 11 (2020): 5135–5145, https://doi.org/10.1021/jacs.9b12440.

[10] T. Zhang, Z. Cai, and S. Chen, “Chemical Trends in the Thermodynamic Stability and Band Gaps of 980 Halide Double Perovskites: A High-Throughput First-Principles Study,” ACS Applied Materials & Interfaces 12, no. 18 (2020): 20680–20690, https://doi.org/10.1021/acsami.0c03622.

[11] J. Greeley, T. F. Jaramillo, J. Bonde, I. Chorkendorff, and J. K. Nørskov, “Computational High-Throughput Screening of Electrocatalytic Materials for Hydrogen Evolution,” Nature Materials 5, no. 11 (2006): 909–913, https://doi.org/10.1038/nmat1752.

[12] S. Wang, Z. Wang, W. Setyawan, N. Mingo, and S. Curtarolo, “Assessing the Thermoelectric Properties of Sintered Compounds via High-Throughput Ab-Initio Calculations,” Physical Review X 1, no. 2 (2011): 021012, https://doi.org/10.1103/PhysRevX.1.021012.

[13] K. Yang, W. Setyawan, S. Wang, M. Buongiorno Nardelli, and S. Curtarolo, “A Search Model for Topological Insulators With High-Throughput Robustness Descriptors,” Nature Materials 11, no. 7 (2012): 614–619, https://doi.org/10.1038/nmat3332.

[14] S. Curtarolo, G. L. W. Hart, M. B. Nardelli, N. Mingo, S. Sanvito, and O. Levy, “The High-Throughput Highway to Computational Materials Design,” Nature Materials 12, no. 3 (2013): 191–201, https://doi.org/10.1038/nmat3568.

[15] R. Gautier, X. Zhang, L. Hu, et al., “Prediction and Accelerated Laboratory Discovery of Previously Unknown 18-Electron ABX Compounds,” Nature Chemistry 7, no. 4 (2015): 308–316, https://doi.org/10.1038/nchem.2207.

[16] S. Sanvito, C. Oses, J. Xue, et al., “Accelerated Discovery of New Magnets in the Heusler Alloy Family,” Science Advances 3, no. 4 (2017): e1602241, https://doi.org/10.1126/sciadv.1602241.

[17] Y. Cai, W. Xie, Y. T. Teng, et al., “High-Throughput Computational Study of Halide Double Perovskite Inorganic Compounds,” Chemistry of Materials 31, no. 15 (2019): 5392–5401, https://doi.org/10.1021/acs.chemmater.9b00116.

[18] T. Nakajima and K. Sawada, “Discovery of Pb-Free Perovskite Solar Cells via High-Throughput Simulation on the K Computer,” Journal of Physical Chemistry Letters 8, no. 19 (2017): 4826–4831, https://doi.org/10.1021/acs.jpclett.7b02203.

[19] X. Cai, Y. Zhang, Z. Shi, et al., “Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities,” Advanced Science 9, no. 4 (2022): 2103648, https://doi.org/10.1002/advs.202103648.

[20] J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, “Materials Design and Discovery With High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD),” Journal of the Minerals 65, no. 11 (2013): 1501–1509, https://doi.org/10.1007/s11837-013-0755-4.

[21] A. Jain, S. P. Ong, G. Hautier, et al., “Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation,” APL Materials 1, no. 1 (2013): 011002, https://doi.org/10.1063/1.4812323.

[22] D. O. Baumann, F. Laufer, J. Roger, R. Singh, M. Gholipoor, and U. W. Paetzold, “Repeatable Perovskite Solar Cells Through Fully Automated Spin-Coating and Quenching,” ACS Applied Materials & Interfaces 16, no. 40 (2024): 54007–54016, https://doi.org/10.1021/acsami.4c13024.

[23] Y. Zhao, T. Heumueller, J. Zhang, et al., “A Bilayer Conducting Polymer Structure for Planar Perovskite Solar Cells With Over 1,400 Hours Operational Stability at Elevated Temperatures,” Nature Energy 7, no. 2 (2022): 144–152, https://doi.org/10.1038/s41560-021-00953-z.

[24] J. Wu, J. Zhang, M. Hu, et al., “Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches,” Journal of the American Chemical Society 145, no. 30 (2023): 16517–16525, https://doi.org/10.1021/jacs.3c03271.

[25] J. Zhang, B. Liu, Z. Liu, et al., “Optimizing Perovskite Thin-Film Parameter Spaces With Machine Learning-Guided Robotic Platform for High-Performance Perovskite Solar Cells,” Advanced Energy Materials 13, no. 48 (2023): 2302594, https://doi.org/10.1002/aenm.202302594.

[26] T. Osterrieder, F. Schmitt, L. Lüer, et al., “Autonomous Optimization of an Organic Solar Cell in a 4-Dimensional Parameter Space,” Energy & Environmental Science 16, no. 9 (2023): 3984–3993, https://doi.org/10.1039/D3EE02027D.

[27] J. Wu, L. Torresi, M. Hu, et al., “Inverse Design Workflow Discovers Hole-Transport Materials Tailored for Perovskite Solar Cells,” Science 386, no. 6727 (2024): 1256–1264, https://doi.org/10.1126/science.ads0901.

[28] H. Wang, T. Fu, Y. Du, et al., “Scientific Discovery in the Age of Artificial Intelligence,” Nature 620, no. 7972 (2023): 47–60, https://doi.org/10.1038/s41586-023-06221-2.

[29] K. T. Butler, K. Choudhary, G. Csanyi, A. M. Ganose, S. V. Kalinin, and D. Morgan, “Setting Standards for Data Driven Materials Science,” Npj Computational Materials 10, no. 1 (2024): 231, https://doi.org/10.1038/s41524-024-01411-6.

[30] L. Zhang, N. Li, D. Liu, et al., “Deep Learning for Additive Screening in Perovskite Light-Emitting Diodes,” Angewandte Chemie International Edition 61, no. 37 (2022): e202209337, https://doi.org/10.1002/anie.202209337.

[31] A. E. Siemenn, E. Aissi, F. Sheng, et al., “Using Scalable Computer Vision to Automate High-Throughput Semiconductor Characterization,” Nature Communications 15, no. 1 (2024): 4654, https://doi.org/10.1038/s41467-024-48768-2.

[32] M. Schilling-Wilhelmi, M. Ríos-García, S. Shabih, et al., “From Text to Insight: Large Language Models for Chemical Data Extraction,” Chemical Society Reviews 54, no. 3 (2025): 1125–1150, https://doi.org/10.1039/d4cs00913d.

[33] L. M. Antunes, K. T. Butler, and R. Grau-Crespo, “Crystal Structure Generation With Autoregressive Large Language Modeling,” Nature Communications 15, no. 1 (2024): 10570, https://doi.org/10.1038/s41467-024-54639-7.

[34] W.-J. Yin, T. Shi, and Y. Yan, “Unusual Defect Physics in CH₃NH₃PbI₃ Perovskite Solar Cell Absorber,” Applied Physics Letters 104, no. 6 (2014): 063903, https://doi.org/10.1063/1.4864778.

[35] C. Freysoldt, B. Grabowski, T. Hickel, et al., “First-Principles Calculations for Point Defects in Solids,” Reviews of Modern Physics 86, no. 1 (2014): 253–305, https://doi.org/10.1103/RevModPhys.86.253.

[36] D. Broberg, B. Medasani, N. E. R. Zimmermann, et al., “PyCDT: A Python Toolkit for Modeling Point Defects in Semiconductors and Insulators,” Computer Physics Communications 226 (2018): 165–179, https://doi.org/10.1016/j.cpc.2018.01.004.

[37] Y. Su, K. Wang, X. Guan, et al., “Machine Learning Descriptors for Mapping Structure-Property-Performance Relationships of Perovskite Solar Cells,” Advanced Energy Materials 16, no. 7 (2025): e05294, https://doi.org/10.1002/aenm.202505294.

[38] J. Heyd, G. E. Scuseria, and M. Ernzerhof, “Hybrid Functionals Based on a Screened Coulomb Potential,” Journal of Chemical Physics 118, no. 18 (2003): 8207–8215, https://doi.org/10.1063/1.1564060.

[39] J. Even, L. Pedesseau, J.-M. Jancu, and C. Katan, “Importance of Spin–Orbit Coupling in Hybrid Organic/Inorganic Perovskites for Photovoltaic Applications,” Journal of Physical Chemistry Letters 4, no. 17 (2013): 2999–3005, https://doi.org/10.1021/jz401532q.

[40] G. Giorgi, J.-I. Fujisawa, H. Segawa, and K. Yamashita, “Small Photocarrier Effective Masses Featuring Ambipolar Transport in Methylammonium Lead Iodide Perovskite: A Density Functional Analysis,” Journal of Physical Chemistry Letters 4, no. 24 (2013): 4213–4216, https://doi.org/10.1021/jz4023865.

[41] L. Huang and W. R. L. Lambrecht, “Electronic Band Structure, Phonons, and Exciton Binding Energies of Halide Perovskites CsSnCl₃, CsSnBr₃, and CsSnI₃,” Physical Review B 88, no. 16 (2013): 165203, https://doi.org/10.1103/PhysRevB.88.165203.

[42] Y. Li and K. Yang, “High-Throughput Computational Design of Organic–Inorganic Hybrid Halide Semiconductors Beyond Perovskites for Optoelectronics,” Energy & Environmental Science 12, no. 7 (2019): 2233–2243, https://doi.org/10.1039/C9EE01371G.

[43] W.-J. Yin, J.-H. Yang, J. Kang, Y. Yan, and S.-H. Wei, “Halide Perovskite Materials for Solar Cells: A Theoretical Review,” Journal of Materials Chemistry A 3, no. 17 (2015): 8926–8942, https://doi.org/10.1039/C4TA05033A.

[44] Z. Xiao and Y. Yan, “Progress in Theoretical Study of Metal Halide Perovskite Solar Cell Materials,” Advanced Energy Materials 7, no. 22 (2017): 1701136, https://doi.org/10.1002/aenm.201701136.

[45] S. Saha, T. P. Sinha, and A. Mookerjee, “Electronic Structure, Chemical Bonding, and Optical Properties of Paraelectric BaTiO₃,” Physical Review B 62, no. 13 (2000): 8828–8834, https://doi.org/10.1103/PhysRevB.62.8828.

[46] G. Giorgi, K. Yamashita, and M. Palummo, “Nature of the Electronic and Optical Excitations of Ruddlesden–Popper Hybrid Organic–Inorganic Perovskites: The Role of the Many-Body Interactions,” Journal of Physical Chemistry Letters 9, no. 19 (2018): 5891–5896, https://doi.org/10.1021/acs.jpclett.8b02653.

[47] Y. Cho and T. C. Berkelbach, “Optical Properties of Layered Hybrid Organic–Inorganic Halide Perovskites: A Tight-Binding GW-BSE Study,” Journal of Physical Chemistry Letters 10, no. 20 (2019): 6189–6196, https://doi.org/10.1021/acs.jpclett.9b02491.

[48] C. J. Bartel, C. Sutton, B. R. Goldsmith, et al., “New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides,” Science Advances 5, no. 2 (2019): eaav0693, https://doi.org/10.1126/sciadv.aav0693.

[49] Z. Xiao, K.-Z. Du, W. Meng, J. Wang, D. B. Mitzi, and Y. Yan, “Intrinsic Instability of Cs2in (I) M (Iii) X6 (M = Bi, Sb; X = Halogen) Double Perovskites: A Combined Density Functional Theory and Experimental Study,” Journal of the American Chemical Society 139, no. 17 (2017): 6054–6057, https://doi.org/10.1021/jacs.7b02227.

[50] Z. Xiao, K. Z. Du, W. Meng, D. B. Mitzi, and Y. Yan, “Chemical Origin of the Stability Difference Between Copper (I)- and Silver (I)-Based Halide Double Perovskites,” Angewandte Chemie 129, no. 40 (2017): 12275–12279, https://doi.org/10.1002/anie.201705113.

[51] S. Lu, Q. Zhou, Y. Ouyang, Y. Guo, Q. Li, and J. Wang, “Accelerated Discovery of Stable Lead-Free Hybrid Organic-Inorganic Perovskites via Machine Learning,” Nature Communications 9 (2018): 3405, https://doi.org/10.1038/s41467-018-05761-w.

[52] T. J. Jacobsson, A. Hultqvist, A. García-Fernández, et al., “An Open-Access Database and Analysis Tool for Perovskite Solar Cells Based on the Fair Data Principles,” Nature Energy 7, no. 1 (2022): 107–115, https://doi.org/10.1038/s41560-021-00941-3.

[53] H. Habib, S. U. Rehman, H. El Hyani, M. N. Sharif, F. Tan, and K.-F. Wang, “Degradation Pathways in Perovskite Solar Cells: Strategies for Enhancing Stability,” Energy Technology 13, no. 11 (2025): 2500137, https://doi.org/10.1002/ente.202500137.

[54] J.-M. Yang, Y. Luo, Q. Bao, Y.-Q. Li, and J.-X. Tang, “Recent Advances in Energetics and Stability of Metal Halide Perovskites for Optoelectronic Applications,” Advanced Materials Interfaces 6, no. 3 (2019): 1801351, https://doi.org/10.1002/admi.201801351.

[55] C.-H. Chiang and C.-G. Wu, “Film Grain-Size Related Long-Term Stability of Inverted Perovskite Solar Cells,” ChemSusChem 9, no. 18 (2016): 2666–2672, https://doi.org/10.1002/cssc.201600887.

[56] M. Khytko, S. Banerjee, K. Křížová, et al., “Grain-Size-Dependent Stability and Crystallographic Orientation Effects in Mafa Perovskite Thin Films,” Journal of Physical Chemistry Letters 16, no. 46 (2025): 11901–11906, https://doi.org/10.1021/acs.jpclett.5c02728.

[57] Y. Zhao, L. Cheng, B. Xu, and L. Meng, “Passivation Effect of Theophylline on the Surface Defects of MAPbI₃ Perovskite,” Computational Materials Science 219 (2023): 112028, https://doi.org/10.1016/j.commatsci.2023.112028.

[58] A. Mannodi-Kanakkithodi and M. Chan, “Data-Driven Design of Novel Halide Perovskite Alloys,” Energy & Environmental Science 15, no. 5 (2022): 1930–1949, https://doi.org/10.1039/d1ee02971a.

[59] R. Jacobs, G. Luo, and D. Morgan, “Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High-Efficiency Solar Cells,” Advanced Functional Materials 29, no. 23 (2019): 1804354, https://doi.org/10.1002/adfm.201804354.

[60] N. Karimitari, W. J. Baldwin, E. W. Muller, et al., “Accurate Crystal Structure Prediction of New 2D Hybrid Organic-Inorganic Perovskites,” Journal of the American Chemical Society 146, no. 40 (2024): 27392–27404, https://doi.org/10.1021/jacs.4c06549.

[61] A. Jain, O. Voznyy, and E. H. Sargent, “High-Throughput Screening of Lead-Free Perovskite-Like Materials for Optoelectronic Applications,” Journal of Physical Chemistry C 121, no. 13 (2017): 7183–7187, https://doi.org/10.1021/acs.jpcc.7b02221.

[62] X. Diao, Y. Diao, Y. Tang, et al., “High-Throughput Screening of Stable and Efficient Double Inorganic Halide Perovskite Materials by DFT,” Scientific Reports 12, no. 1 (2022): 12633, https://doi.org/10.1038/s41598-022-16221-3.

[63] M. R. Filip, X. Liu, A. Miglio, G. Hautier, and F. Giustino, “Phase Diagrams and Stability of Lead-Free Halide Double Perovskites Cs₂BB′ X6: B = Sb and Bi, B′ = Cu, Ag, and Au, and X = Cl, Br, and I,” Journal of Physical Chemistry C 122, no. 1 (2018): 158–170, https://doi.org/10.1021/acs.jpcc.7b10370.

[64] X. G. Zhao, J. H. Yang, Y. Fu, et al., “Design of Lead-Free Inorganic Halide Perovskites for Solar Cells via Cation-Transmutation,” Journal of the American Chemical Society 139, no. 7 (2017): 2630–2638, https://doi.org/10.1021/jacs.6b09645.

[65] D. Yang, J. Lv, X. Zhao, et al., “Functionality-Directed Screening of Pb-Free Hybrid Organic-Inorganic Perovskites With Desired Intrinsic Photovoltaic Functionalities,” Chemistry of Materials 29, no. 2 (2017): 524–538, https://doi.org/10.1021/acs.chemmater.6b03221.

[66] X. Wang, Y. Gao, E. Krzystowczyk, et al., “High-Throughput Oxygen Chemical Potential Engineering of Perovskite Oxides for Chemical Looping Applications,” Energy & Environmental Science 15, no. 4 (2022): 1512–1528, https://doi.org/10.1039/d1ee02889h.

[67] N. T. P. Hartono, J. Thapa, A. Tiihonen, et al., “How Machine Learning Can Help Select Capping Layers to Suppress Perovskite Degradation,” Nature Communications 11, no. 1 (2020): 4172, https://doi.org/10.1038/s41467-020-17945-4.

[68] Z. Li, C. Huang, L. Chao, Y. Chen, W. Huang, and G. Chen, “Perovskite Solar Cells Empowered by Machine Learning,” Journal of Energy Chemistry 109 (2025): 403–437, https://doi.org/10.1016/j.jechem.2025.05.029.

[69] Y. Lu, D. Wei, W. Liu, et al., “Predicting the Device Performance of the Perovskite Solar Cells From the Experimental Parameters Through Machine Learning of Existing Experimental Results,” Journal of Energy Chemistry 77 (2023): 200–208, https://doi.org/10.1016/j.jechem.2022.10.024.

[70] W. Liu, N. Meng, X. Huo, et al., “Machine Learning Enables Intelligent Screening of Interface Materials Towards Minimizing Voltage Losses for P-I-N Type Perovskite Solar Cells,” Journal of Energy Chemistry 83 (2023): 128–137, https://doi.org/10.1016/j.jechem.2023.04.015.

[71] W. Rahman, C. Zhong, H. Liu, et al., “Enhancing Perovskite Solar Cell Efficiency and Stability: A Multimodal Prediction Approach Integrating Microstructure, Composition, and Processing Technology,” Nanoscale 17, no. 26 (2025): 15935–15949, https://doi.org/10.1039/D5NR00364D.

[72] X. Zhu, Y. Sun, H. Meng, et al., “Dual-Model Machine Learning for Collaborative Optimization of Efficiency and Stability in Perovskite Solar Cells,” Chemical Engineering Journal 524 (2025): 169705, https://doi.org/10.1016/j.cej.2025.169705.

[73] C. She, Q. Huang, C. Chen, Y. Jiang, Z. Fan, and J. Gao, “Machine Learning-Guided Search for High-Efficiency Perovskite Solar Cells With Doped Electron Transport Layers,” Journal of Materials Chemistry A 9, no. 44 (2021): 25168–25177, https://doi.org/10.1039/D1TA08194B.

[74] A. Valencia, F. Liu, X. Zhang, X. Bo, W. Li, and W. A. Daoud, “Auto-Generating a Database on the Fabrication Details of Perovskite Solar Devices,” Scientific Data 12, no. 1 (2025): 270, https://doi.org/10.1038/s41597-025-04566-z.

[75] P. Ball, “Is AI Leading to a Reproducibility Crisis in Science?,” Nature 624, no. 7990 (2023): 22–25, https://doi.org/10.1038/d41586-023-03817-6.

[76] B. Basu, N. H. Gowtham, Y. Xiao, S. R. Kalidindi, and K. W. Leong, “Biomaterialomics: Data Science-Driven Pathways to Develop Fourth-Generation Biomaterials,” Acta Biomaterialia 143 (2022): 1–25, https://doi.org/10.1016/j.actbio.2022.02.027.

[77] S. W. Cranford, J. de Boer, C. van Blitterswijk, and M. J. Buehler, “Materiomics: An -Omics Approach to Biomaterials Research,” Advanced Materials 25, no. 6 (2013): 802–824, https://doi.org/10.1002/adma.201202553.

[78] A. V. Sadybekov and V. Katritch, “Computational Approaches Streamlining Drug Discovery,” Nature 616, no. 7958 (2023): 673–685, https://doi.org/10.1038/s41586-023-05905-z.

[79] P. W. Hatfield, J. A. Gaffney, G. J. Anderson, et al., “The Data-Driven Future of High-Energy-Density Physics,” Nature 593, no. 7859 (2021): 351–361, https://doi.org/10.1038/s41586-021-03382-w.

[80] J. Kerner, A. Dogan, and H. von Recum, “Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research,” Acta Biomaterialia 130 (2021): 54–65, https://doi.org/10.1016/j.actbio.2021.05.053.

[81] N. David, W. Sun, and C. W. Coley, “The Promise and Pitfalls of AI for Molecular and Materials Synthesis,” Nature Computational Science 3, no. 5 (2023): 362–364, https://doi.org/10.1038/s43588-023-00446-x.

[82] G. Liu, Y. Zeng, T. Lv, et al., “High-Throughput Preparation of Radioprotective Polymers via Hantzsch's Reaction for In Vivo X-Ray Damage Determination,” Nature Communications 11, no. 1 (2020): 6214, https://doi.org/10.1038/s41467-020-20027-0.

[83] Z. Chen, Y. Suzuki, A. Imayoshi, et al., “Solvent-Free Autocatalytic Supramolecular Polymerization,” Nature Materials 21, no. 2 (2022): 253–261, https://doi.org/10.1038/s41563-021-01122-z.

[84] D. G. Rivera, M. G. Ricardo, A. V. Vasco, L. A. Wessjohann, and E. V. Van der Eycken, “On-Resin Multicomponent Protocols for Biopolymer Assembly and Derivatization,” Nature Protocols 16, no. 2 (2021): 561–578, https://doi.org/10.1038/s41596-020-00445-6.

[85] D. G. Anderson, D. M. Lynn, and R. Langer, “Semi-Automated Synthesis and Screening of a Large Library of Degradable Cationic Polymers for Gene Delivery,” Angewandte Chemie International Edition 42, no. 27 (2003): 3153–3158, https://doi.org/10.1002/anie.200351244.

[86] J. J. Hanak, “The ‘Multiple-Sample Concept’ in Materials Research: Synthesis, Compositional Analysis and Testing of Entire Multicomponent Systems,” Journal of Materials Science 5, no. 11 (1970): 964–971, https://doi.org/10.1007/BF00558177.

[87] B. Burger, P. M. Maffettone, V. V. Gusev, et al., “A Mobile Robotic Chemist,” Nature 583, no. 7815 (2020): 237–241, https://doi.org/10.1038/s41586-020-2442-2.

[88] A. Vasilevich and J. de Boer, “Robot-Scientists Will Lead Tomorrow's Biomaterials Discovery,” Current Opinion in Biomedical Engineering 6 (2018): 74–80, https://doi.org/10.1016/j.cobme.2018.03.005.

[89] S. Baek, S. Song, J. Lee, and J. M. Kim, “Nanoscale Diameter Control of Sensory Polydiacetylene Nanoparticles on Microfluidic Chip for Enhanced Fluorescence Signal,” Sensors and Actuators B: Chemical 230 (2016): 623–629, https://doi.org/10.1016/j.snb.2016.02.065.

[90] Y. Liu, J. Du, J. Choi, et al., “A High-Throughput Platform for Formulating and Screening Multifunctional Nanoparticles Capable of Simultaneous Delivery of Genes and Transcription Factors,” Angewandte Chemie 128, no. 1 (2016): 177–181, https://doi.org/10.1002/anie.201507546.

[91] N. Boehnke, J. P. Straehla, H. C. Safford, et al., “Massively Parallel Pooled Screening Reveals Genomic Determinants of Nanoparticle Delivery,” Science 377, no. 6604 (2022): eabm5551, https://doi.org/10.1126/science.abm5551.

[92] K. Toth, C. O. Osuji, K. G. Yager, and G. S. Doerk, “High-Throughput Morphology Mapping of Self-Assembling Ternary Polymer Blends,” RSC Advances 10, no. 69 (2020): 42529–42541, https://doi.org/10.1039/d0ra08491c.

[93] A. W. Salamon, “The Current World of Nanomaterial Characterization: Discussion of Analytical Instruments for Nanomaterial Characterization,” Environmental Engineering Science 30, no. 3 (2013): 101–108, https://doi.org/10.1089/ees.2012.0330.

[94] M. Liebel, I. Calderon, N. Pazos-Perez, N. F. van Hulst, and R. A. Alvarez-Puebla, “Widefield SERS for High-Throughput Nanoparticle Screening,” Angewandte Chemie International Edition 61, no. 20 (2022): e202200072, https://doi.org/10.1002/anie.202200072.

[95] J. Lu, H. Jiang, Y. Yan, Z. Zhu, F. Zheng, and Q. Sun, “High-Throughput Preparation of Supramolecular Nanostructures on Metal Surfaces,” ACS Nano 16, no. 8 (2022): 13160–13167, https://doi.org/10.1021/acsnano.2c06294.

[96] C. R. Laramy, K. A. Brown, M. N. O'Brien, and C. A. Mirkin, “High-Throughput, Algorithmic Determination of Nanoparticle Structure From Electron Microscopy Images,” ACS Nano 9, no. 12 (2015): 12488–12495, https://doi.org/10.1021/acsnano.5b05968.

[97] I. Y. Perevyazko, J. T. Delaney, A. Vollrath, G. M. Pavlov, S. Schubert, and U. S. Schubert, “Examination and Optimization of the Self-Assembly of Biocompatible, Polymeric Nanoparticles by High-Throughput Nanoprecipitation,” Soft Matter 7, no. 10 (2011): 5030–5035, https://doi.org/10.1039/c1sm05079f.

[98] Y. An, C. Perini, J. Hidalgo, et al., “Identifying High-Performance and Durable Methylammonium-Free Lead Halide Perovskites via High-Throughput Synthesis and Characterization,” Energy & Environmental Science 14, no. 12 (2021): 6638–6654, https://doi.org/10.1039/d1ee02691g.

[99] J. Yang, B. Lawrie, S. Kalinin, and M. Ahmadi, “High-Throughput Automated Exploration of Phase Growth Behaviors in Quasi-2D Formamidinium Metal Halide Perovskites,” Advanced Energy Materials 13, no. 43 (2023): 2302337, https://doi.org/10.1002/aenm.202302337.

[100] S. Chen, Y. Hou, H. Chen, et al., “Exploring the Stability of Novel Wide Bandgap Perovskites by a Robot Based High Throughput Approach,” Advanced Energy Materials 8, no. 6 (2018): 1701543, https://doi.org/10.1002/aenm.201701543.

[101] J. Zhang, J. Wu, S. Langner, et al., “Exploring the Steric Hindrance of Alkylammonium Cations in the Structural Reconfiguration of Quasi-2D Perovskite Materials Using a High-Throughput Experimental Platform,” Advanced Functional Materials 32, no. 43 (2022): 2207101, https://doi.org/10.1002/adfm.202207101.

[102] O. Stroyuk, O. Raievska, M. Daum, J. Hauch, and C. J. Brabec, “Six Metal Cations in One Double Perovskite: Exploring Complexity of Chloride Elpasolites by High-Throughput Experimentation,” Journal of Materials Chemistry C 12, no. 24 (2024): 8705–8718, https://doi.org/10.1039/d4tc01693a.

[103] F. Sheng, K. Ji, L. Dai, et al., “High-Throughput Micro-Scale Bandgap Mapping for Perovskite-Inspired Materials With Complex Composition Space,” Nature Communications 16, no. 1 (2025): 7477, https://doi.org/10.1038/s41467-025-62774-y.

[104] Z. Xu, Y. Zhao, J. Zhang, K. Chen, C. J. Brabec, and Y. Feng, “Phase Diagram and Stability of Mixed-Cation Lead Iodide Perovskites: A Theory and Experiment Combined Study,” Physical Review Materials 4, no. 9 (2020): 095401, https://doi.org/10.1103/PhysRevMaterials.4.095401.

[105] J. Zhang, J. A. Hauch, and C. J. Brabec, “Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies,” Accounts of Chemical Research 57, no. 9 (2024): 1434–1445, https://doi.org/10.1021/acs.accounts.4c00095.

[106] J. Zhang, V. M. Le Corre, J. Wu, et al., “Autonomous Optimization of Air-Processed Perovskite Solar Cell in a Multidimensional Parameter Space,” Advanced Energy Materials 15, no. 19 (2025): 2404957, https://doi.org/10.1002/aenm.202404957.

[107] J. Zhang, J. Wu, A. Barabash, et al., “Precise Control of Process Parameters for > 23% Efficiency Perovskite Solar Cells in Ambient Air Using an Automated Device Acceleration Platform,” Energy & Environmental Science 17, no. 15 (2024): 5490–5499, https://doi.org/10.1039/d4ee01432d.

[108] E. Gu, X. Tang, S. Langner, et al., “Robot-Based High-Throughput Screening of Antisolvents for Lead Halide Perovskites,” Joule 4, no. 8 (2020): 1806–1822, https://doi.org/10.1016/j.joule.2020.06.013.

[109] Y. Deng, Z. Ni, A. F. Palmstrom, et al., “Reduced Self-Doping of Perovskites Induced by Short Annealing for Efficient Solar Modules,” Joule 4, no. 9 (2020): 1949–1960, https://doi.org/10.1016/j.joule.2020.07.003.

[110] N. Meftahi, M. A. Surmiak, S. O. Fürer, et al., “Machine Learning Enhanced High-Throughput Fabrication and Optimization of Quasi-2D Ruddlesden-Popper Perovskite Solar Cells,” Advanced Energy Materials 13, no. 38 (2023): 2370154, https://doi.org/10.1002/aenm.202203859.

[111] Z. Liu, N. Rolston, A. C. Flick, et al., “Machine Learning With Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing,” Joule 6, no. 4 (2022): 834–849, https://doi.org/10.1016/j.joule.2022.03.003.

[112] J. G. Manion, A. H. Proppe, G. Hicks, E. H. Sargent, and D. S. Seferos, “High-Throughput Screening of Antisolvents for the Deposition of High-Quality Perovskite Thin Films,” ACS Applied Materials & Interfaces 12, no. 23 (2020): 26026–26032, https://doi.org/10.1021/acsami.0c06110.

[113] J. Zhang, S. Langner, J. Wu, et al., “Intercalating-Organic-Cation-Induced Stability Bowing in Quasi-2D Metal-Halide Perovskites,” ACS Energy Letters 7, no. 1 (2021): 70–77, https://doi.org/10.1021/acsenergylett.1c02081.

[114] Y. Zhao, J. Zhang, Z. Xu, et al., “Discovery of Temperature-Induced Stability Reversal in Perovskites Using High-Throughput Robotic Learning,” Nature Communications 12, no. 1 (2021): 2191, https://doi.org/10.1038/s41467-021-22472-x.

[115] A. Wieczorek, A. G. Kuba, J. Sommerhäuser, L. N. Caceres, C. M. Wolff, and S. Siol, “Advancing High-Throughput Combinatorial Aging Studies of Hybrid Perovskite Thin Films via Precise Automated Characterization Methods and Machine Learning Assisted Analysis,” Journal of Materials Chemistry A 12, no. 12 (2024): 7025–7035, https://doi.org/10.1039/d3ta07274f.

[116] H. Hu, H. Shi, J. Zhang, J. A. Hauch, A. Osvet, and C. J. Brabec, “Automated Synthesis of 1-Tetradecylphosphonic Acid-Capped FAPbBr₃ Nanocrystals for Light Conversion in Display Applications,” ACS Applied Nano Materials 7, no. 8 (2024): 8823–8829, https://doi.org/10.1021/acsanm.4c00283.

[117] R. Upadhya, S. Kosuri, M. Tamasi, et al., “Automation and Data-Driven Design of Polymer Therapeutics,” Advanced Drug Delivery Reviews 171 (2021): 1–28, https://doi.org/10.1016/j.addr.2020.11.009.

[118] W. H. Chen, Q. W. Chen, Q. Chen, et al., “Biomedical Polymers: Synthesis, Properties, and Applications,” Science China Chemistry 65, no. 6 (2022): 1010–1075, https://doi.org/10.1007/s11426-022-1243-5.

[119] C. Li, L. Wang, Q. Yan, F. Liu, Y. Shen, and Z. Li, “Rapid and Controlled Polymerization of Bio-Sourced Δ-Caprolactone Toward Fully Recyclable Polyesters and Thermoplastic Elastomers,” Angewandte Chemie International Edition 61, no. 16 (2022): e202201407, https://doi.org/10.1002/anie.202201407.

[120] Q. Qu, S. Gao, F. Wu, et al., “Synthesis of Disulfide Surrogate Peptides Incorporating Large-Span Surrogate Bridges Through a Native-Chemical-Ligation-Assisted Diaminodiacid Strategy,” Angewandte Chemie 132, no. 15 (2020): 6093–6101, https://doi.org/10.1002/anie.201915358.

[121] M. X. Li, S. F. Zhao, Z. Lu, et al., “High-Temperature Bulk Metallic Glasses Developed by Combinatorial Methods,” Nature 569, no. 7754 (2019): 99–103, https://doi.org/10.1038/s41586-019-1145-z.

[122] L. Miao, L. Li, Y. Huang, et al., “Delivery of mRNA Vaccines With Heterocyclic Lipids Increases Anti-Tumor Efficacy by Sting-Mediated Immune Cell Activation,” Nature Biotechnology 37, no. 10 (2019): 1174–1185, https://doi.org/10.1038/s41587-019-0247-3.

[123] D. Chen, K. T. Love, Y. Chen, et al., “Rapid Discovery of Potent siRNA-Containing Lipid Nanoparticles Enabled by Controlled Microfluidic Formulation,” Journal of the American Chemical Society 134, no. 16 (2012): 6948–6951, https://doi.org/10.1021/ja301621z.

[124] J. Chen, Z. Ye, C. Huang, et al., “Lipid Nanoparticle-Mediated Lymph Node-Targeting Delivery of mRNA Cancer Vaccine Elicits Robust CD8⁺ T Cell Response,” Proceedings of the National Academy of Sciences of the United States of America 119, no. 34 (2022): e2207841119, https://doi.org/10.1073/pnas.2207841119.

[125] B. P. MacLeod, F. G. L. Parlane, T. D. Morrissey, et al., “Self-Driving Laboratory for Accelerated Discovery of Thin-Film Materials,” Science Advances 6, no. 20 (2020): eaaz8867, https://doi.org/10.1126/sciadv.aaz8867.

[126] K. Choudhary, Q. Zhang, A. C. E. Reid, et al., “Computational Screening of High-Performance Optoelectronic Materials Using OptB88vdW and TB-mBJ Formalisms,” Scientific Data 5 (2018): 180082, https://doi.org/10.1038/sdata.2018.82.

[127] S. Lany, “Semiconducting Transition Metal Oxides,” Journal of Physics: Condensed Matter 27, no. 28 (2015): 283203, https://doi.org/10.1088/0953-8984/27/28/283203.

[128] L. Zhang, Q. Bing, H. Qin, L. Yu, H. Li, and D. Deng, “Artificial Intelligence for Catalyst Design and Synthesis,” Matter 8, no. 5 (2025): 102138, https://doi.org/10.1016/j.matt.2025.102138.

[129] A. Bin Faheem, Z. Han, D. Wu, and H. Li, “AI-Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries,” Advanced Materials 38, no. 11 (2026): e21975, https://doi.org/10.1002/adma.202521975.

[130] R. Jinnouchi, F. Karsai, and G. Kresse, “Making Free-Energy Calculations Routine: Combining First Principles With Machine Learning,” Physical Review B 101, no. 6 (2020): 060201, https://doi.org/10.1103/PhysRevB.101.060201.

[131] Y. Li, D. Maldonado-Lopez, V. Ríos Vargas, J. Zhang, and K. Yang, “Stability Diagrams, Defect Tolerance, and Absorption Coefficients of Hybrid Halide Semiconductors: High-Throughput First-Principles Characterization,” Journal of Chemical Physics 152, no. 8 (2020): 084106, https://doi.org/10.1063/1.5127929.

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