Machine learning-enabled optoelectronic material discovery: a comprehensive review

Abstract

The development of advanced optoelectronic materials constitutes a pivotal frontier in modern energy and communication technologies, facilitating critical energy-photon-electron interconversion processes that underpin sustainable energy infrastructures and high-performance electronic devices. However, the discovery and optimization of novel optoelectronic materials face substantial hurdles arising from complicated structure-property interdependencies, prohibitive development costs, and protracted innovation cycles. Conventional empirical approaches and computational simulations usually exhibit limited efficacy in addressing the escalating demands for materials with superior stability, economic viability, and customizable electronic properties. The integration of machine learning (ML) with high-throughput screening has emerged as a transformative strategy to address these challenges. By rapidly processing large multidimensional datasets and predicting critical material properties such as electronic structure, thermodynamic stability, and charge transport behaviors, ML offers unprecedented capabilities in the efficient and rational design of high-performance optoelectronic materials. This review provides a comprehensive overview of cutting-edge ML-driven methodologies in efficient optoelectronic materials discovery with emphasis on critical workflows, data integration strategies, and model frameworks. We also discuss the challenges and prospects for ML applications, particularly in data standardization, model interpretability and closed-loop experimental validation. We further propose the potential of artificial intelligence and autonomous laboratories to build a powerful discovery pipeline to advance the development of high-performance optoelectronic materials.

Keywords

Optoelectronic materials / machine learning / high-throughput calculation / materials design

References

[1] Zhang L,Wang K.Advances in the application of perovskite materials.Nanomicro Lett2023;15:177 PMCID:PMC10333173

[2] Liu J,Wang W.Photoelectrocatalytic principles for meaningfully studying photocatalyst properties and photocatalysis processes: from fundamental theory to environmental applications.J Energy Chem2023;86:84-117

[3] Tan S,Yavuz I.Stability-limiting heterointerfaces of perovskite photovoltaics.Nature2022;605:268-73

[4] Lu T,Lu W.Recent progress in the data-driven discovery of novel photovoltaic materials.J Mater Inf2022;2:7

[5] Wen J,Jiang L.Copper-based perovskites and perovskite-like halides: a review from the perspective of molecular level.Nano Energy2024;128:109802

[6] Aydin E,Yildirim BK.Enhanced optoelectronic coupling for perovskite/silicon tandem solar cells.Nature2023;623:732-8

[7] Isikgor FH,Merino LVT,Mcculloch I.Molecular engineering of contact interfaces for high-performance perovskite solar cells.Nat Rev Mater2023;8:89-108

[8] Zhang Z.Light-emitting materials for wearable electronics.Nat Rev Mater2022;7:839-40

[9] Jang E.Review: quantum dot light-emitting diodes.Chem Rev2023;123:4663-92

[10] Han T,Dong Y,Sargent EH.A roadmap for the commercialization of perovskite light emitters.Nat Rev Mater2022;7:757-77

[11] Xiong R,Wen C,Ang YS.Ferroelectric switching driven photocatalytic overall water splitting in the As/In₂Se₃ heterostructure.J Mater Chem A2025;13:4563-75

[12] Chen Z,Chu C.Photocatalytic H₂O₂ production systems: design strategies and environmental applications.Chem Eng J2023;451:138489

[13] Zhou P,Ma Y.Solar-to-hydrogen efficiency of more than 9% in photocatalytic water splitting.Nature2023;613:66-70

[14] Fan Y,Zhu F.Dispersion-assisted high-dimensional photodetector.Nature2024;630:77-83

[15] Li Z,Fang X.Low-dimensional wide-bandgap semiconductors for UV photodetectors.Nat Rev Mater2023;8:587-603

[16] Wang H,Li D.Van der Waals integration based on two-dimensional materials for high-performance infrared photodetectors.Adv Funct Mater2021;31:2103106

[17] Saliba M,Seo JY.Cesium-containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency.Energy Environ Sci2016;9:1989-97 PMCID:PMC4936376

[18] Zhang Y,Liu G.Nonalloyed α-phase formamidinium lead triiodide solar cells through iodine intercalation.Science2025;387:284-90

[19] Tan T,Wang C,Zhang H.2D material optoelectronics for information functional device applications: status and challenges.Adv Sci2020;7:2000058 PMCID:PMC7284198

[20] Dastgeer G,Nazir G.p-GeSe/n-ReS₂ heterojunction rectifier exhibiting a fast photoresponse with ultra-high frequency-switching applications.Adv Mater Inter2021;8:2100705

[21] Liu Z,Tian F,Li J.Computational functionality-driven design of semiconductors for optoelectronic applications.InfoMat2020;2:879-904

[22] Khan D,Muhammad I,Xu Z.Overcoming two key challenges in monolithic perovskite-silicon tandem solar cell development: wide bandgap and textured substrate - a comprehensive review.Adv Energy Mater2023;13:2302124

[23] Zhang CZ.Applications and potentials of machine learning in optoelectronic materials research: an overview and perspectives.Chinese Phys B2023;32:126103

[24] Hu Y,Wei Z,Zhao Y.Recent advances and applications of machine learning in electrocatalysis.J Mater Inf2023;3:18

[25] Yuan J,Yang Y.Applications of machine learning method in high-performance materials design: a review.J Mater Inf2024;4:14

[26] Liu Y,Ju W.Materials discovery and design using machine learning.J Materiomics2017;3:159-77

[27] Yang X,He X.Methods and applications of machine learning in computational design of optoelectronic semiconductors.Sci China Mater2024;67:1042-81

[28] Himanen L,Foster AS.Data-driven materials science: status, challenges, and perspectives.Adv Sci2019;6:1900808 PMCID:PMC6839624

[29] Brunton SL.Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press: 2019.

[30] Chen Z.Data-driven design of eutectic high entropy alloys.J Mater Inf2023;3:10

[31] Chen M,Shan Z.Application of machine learning in perovskite materials and devices: a review.J Energy Chem2024;94:254-72

[32] He H,Qi Y,Li Y.From prediction to design: recent advances in machine learning for the study of 2D materials.Nano Energy2023;118:108965

[33] Chen J,Zha C,Zhang L.Machine learning-driven design of promising perovskites for photovoltaic applications: a review.Surf Interfaces2022;35:102470

[34] Nematov D. Machine learning - driven materials discovery: unlocking next-generation functional materials - a minireview. arXiv 2025, arXiv:2503.18975. https://doi.org/10.48550/arXiv.2503.18975. (accessed 27 May 2025)

[35] Li Y.High-throughput computational design of halide perovskites and beyond for optoelectronics.WIREs Comput Mol Sci2021;11:e1500

[36] Shen L,Yang T,Feng YP.High-throughput computational discovery and intelligent design of two-dimensional functional materials for various applications.Acc Mater Res2022;3:572-83

[37] Xu D,Huo X,Yang M.Advances in data-assisted high-throughput computations for material design.MGE Adv2023;1:e11

[38] Gan Y,Lan P,Elliott SR.Robust design of high-performance optoelectronic chalcogenide crystals from high-throughput computation.J Am Chem Soc2022;144:5878-86

[39] Lan P,Gan Y.High-throughput computational design of 2D ternary chalcogenides for sustainable energy.J Phys Chem Lett2023;14:10489-98

[40] Bai S,Zhao LD.Rethinking SnSe thermoelectrics from computational materials science.Acc Chem Res2023;56:3065-75

[41] Deng T,Yin T.High-throughput strategies in the discovery of thermoelectric materials.Adv Mater2024;36:e2311278

[42] Xu Y,Song ZD.High-throughput calculations of magnetic topological materials.Nature2020;586:702-7

[43] Cao G,Ghiringhelli LM.Artificial intelligence for high-throughput discovery of topological insulators: the example of alloyed tetradymites.Phys Rev Mater2020;4:034204

[44] Zhang X,Liu Y,Liu G.Magnetic electrides: high-throughput material screening, intriguing properties, and applications.J Am Chem Soc2023;145:5523-35

[45] Miao N.Computational design of two-dimensional magnetic materials.WIREs Comput Mol Sci2022;12:e1545

[46] de Pablo JJ,Webb MA.New frontiers for the materials genome initiative.npj Comput Mater2019;5:173

[47] de Pablo, J. J.; Jones, B.; Kovacs, C. L.; Ozolins, V.; Ramirez, A. P. The Materials Genome Initiative, the interplay of experiment, theory and computation.Curr Opin Solid State Mater Sci2014;18:99-117

[48] Yu Q,Leung C,Ren Y.AI in single-atom catalysts: a review of design and applications.J Mater Inf2025;5:9

[49] Jordan MI.Machine learning: trends, perspectives, and prospects.Science2015;349:255-60

[50] Xu P,Li M.Small data machine learning in materials science.npj Comput Mater2023;9:1000

[51] Butler KT,Cartwright H,Walsh A.Machine learning for molecular and materials science.Nature2018;559:547-55

[52] Schleder GR,Acosta CM,Fazzio A.From DFT to machine learning: recent approaches to materials science - a review.J Phys Mater2019;2:032001

[53] Jacobsson TJ,García-Fernández A.An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles.Nat Energy2022;7:107-15

[54] Mannodi-Kanakkithodi A.Data-driven design of novel halide perovskite alloys.Energy Environ Sci2022;15:1930-49

[55] Ma B,Zhao C.An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K_0.5Na_0.5)NbO₃-based ceramics.npj Comput Mater2023;9:1187

[56] Cheng G,Yin WJ.Crystal structure prediction by combining graph network and optimization algorithm.Nat Commun2022;13:1492 PMCID:PMC8938491

[57] Chen C,Ye W,Deng Z.A critical review of machine learning of energy materials.Adv Energy Mater2020;10:1903242

[58] Groom CR,Lightfoot MP.The Cambridge Structural Database.Acta Crystallogr B Struct Sci Cryst Eng Mater2016;72:171-9 PMCID:PMC4822653

[59] Bergerhoff G,Sievers R.The inorganic crystal structure data base.J Chem Inf Comput Sci1983;23:66-9

[60] Gražulis S,Merkys A.Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration.Nucleic Acids Res2012;40:D420-7 PMCID:PMC3245043

[61] Curtarolo S,Wang S.AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations.Comput Mater Sci2012;58:227-35

[62] Jain A,Hautier G.Commentary: The Materials Project: a materials genome approach to accelerating materials innovation.APL Mater2013;1:011002

[63] Gjerding MN,Rasmussen A.Recent progress of the Computational 2D Materials Database (C2DB).2D Mater2021;8:044002

[64] Kirklin S,Meredig B.The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies.npj Comput Mater2015;1:BFnpjcompumats201510

[65] Damewood J,Lunger JR.Representations of materials for machine learning.Annu Rev Mater Res2023;53:399-426

[66] Li S,Chen D,Nie Z.Encoding the atomic structure for machine learning in materials science.WIREs Comput Mol Sci2022;12:e1558

[67] Oh SHV,Kim K,Soon A.Using feature-assisted machine learning algorithms to boost polarity in lead-free multicomponent niobate alloys for high-performance ferroelectrics.Adv Sci2022;9:e2104569 PMCID:PMC9434731

[68] Schmidt J,Botti S.Recent advances and applications of machine learning in solid-state materials science.npj Comput Mater2019;5:221

[69] Li J,Wang S.Feature selection: a data perspective.ACM Comput Surv2018;50:1-45

[70] Hsu H,Lu M.Hybrid feature selection by combining filters and wrappers.Expert Syst Appl2011;38:8144-50

[71] Rodriguez-Galiano VF,Chica-Olmo M.Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods.Sci Total Environ2018;624:661-72

[72] Liu H,Liu Q.An embedded feature selection method for imbalanced data classification.IEEE/CAA J Autom Sinica2019;6:703-15

[73] Zhang Z,Xiong Q.Strategic integration of machine learning in the design of excellent hybrid perovskite solar cells.J Phys Chem Lett2025;16:738-46

[74] Gladkikh V,Hajibabaei A,Myung CW.Machine learning for predicting the band gaps of ABX₃ perovskites from elemental properties.J Phys Chem C2020;124:8905-18

[75] Gou F,Yang Q.Machine learning-assisted prediction and control of bandgap for organic-inorganic metal halide perovskites.ACS Appl Mater Interfaces2025;17:18383-93

[76] Wang AY,Kauwe SK.Machine learning for materials scientists: an introductory guide toward best practices.Chem Mater2020;32:4954-65

[77] Wei J,Sun X.Machine learning in materials science.InfoMat2019;1:338-58

[78] Orupattur NV,Prasad V.Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods.Comput Mater Sci2020;174:109474

[79] Ali Y,Al-Razgan M.Hyperparameter search for machine learning algorithms for optimizing the computational complexity.Processes2023;11:349

[80] Cherkassky V.Practical selection of SVM parameters and noise estimation for SVM regression.Neural Netw2004;17:113-26

[81] Cover T.Nearest neighbor pattern classification.IEEE Trans Inform Theory1967;13:21-7

[82] Yang FJ.An implementation of Naive Bayes Classifier. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, USA. Dec 12-14, 2018. IEEE; 2018; pp. 301-6.

[83] Liu Y,Cui G.Machine learning boosting the development of advanced lithium batteries.Small Methods2021;5:e2100442

[84] Dong X,Cao W,Ma Q.A survey on ensemble learning.Front Comput Sci2020;14:241-58

[85] Sagi O.Ensemble learning: a survey.WIREs Data Min Knowl2018;8:e1249

[86] Friedman JH.Greedy function approximation: a gradient boosting machine.Ann Stat2001;29:1189-232http://www.jstor.org/stable/2699986. (accessed 27 May 2025)

[87] Freund Y. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference, 1996. pp. 148-56. http://www.jstor.org/stable/2699986. (accessed 27 May 2025)

[88] Chen T.XGBoost: a scalable tree boosting system. In Proceedings of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA. 2016; pp. 785-94.

[89] Pavlyshenko B.Using stacking approaches for machine learning models. In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, Aug 21-25, 2018. IEEE; 2018. pp. 255-8.

[90] LeCun Y,Hinton G.Deep learning.Nature2015;521:436-44

[91] Liu J,Su B.Transformative strategies in photocatalyst design: merging computational methods and deep learning.J Mater Inf2024;4:33

[92] Choudhary K,Chen C.Recent advances and applications of deep learning methods in materials science.npj Comput Mater2022;8:734

[93] Jain AK,Mohiuddin KM.Artificial neural networks: a tutorial.Computer1996;29:31-44

[94] Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation.IEEE Trans Pattern Anal Mach Intell2017;39:640-51

[95] Zhang H,Liu D.A comprehensive review of stability analysis of continuous-time recurrent neural networks.IEEE Trans Neural Netw Learning Syst2014;25:1229-62

[96] Goodfellow IJ,Mirza M. Generative adversarial networks. arXiv 2014, arXiv:1406.2661. https://doi.org/10.48550/arXiv.1406.2661. (accessed 27 May 2025)

[97] Zheng Z,Borgs C,Yaghi OM.ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis.J Am Chem Soc2023;145:18048-62 PMCID:PMC11073615

[98] OpenAI: Optimizing language models for dialogue. 2023. https://openai.com/blog/chatgpt/. (accessed 27 May 2025)

[99] Chowdhary KR.Natural language processing. In: Fundamentals of artificial intelligence. New Delhi: Springer India; 2020. pp. 603-49.

[100] Zhu JJ,Ren ZJ.Machine learning in environmental research: common pitfalls and best practices.Environ Sci Technol2023;57:17671-89

[101] Li Z,Zhang R.Machine learning in concrete science: applications, challenges, and best practices.npj Comput Mater2022;8:810

[102] Artrith N,Coudert FX.Best practices in machine learning for chemistry.Nat Chem2021;13:505-8

[103] Wong T.Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation.Pattern Recognit2015;48:2839-46

[104] Efron B. An introduction to the bootstrap. Chapman and Hall/CRC: 1994. https://www.hms.harvard.edu/bss/neuro/bornlab/nb204/statistics/bootstrap.pdf. (accessed 27 May 2025)

[105] Palanivinayagam A,Damaševičius R.Twenty years of machine-learning-based text classification: a systematic review.Algorithms2023;16:236

[106] Sebastiani F.Machine learning in automated text categorization.ACM Comput Surv2002;34:1-47

[107] Chicco D.The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC Genomics2020;21:6 PMCID:PMC6941312

[108] Ho SY,Wong L.Extensions of the external validation for checking learned model interpretability and generalizability.Patterns2020;1:100129 PMCID:PMC7691387

[109] Xiong Z,Liu Z,Hu M.Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation.Comput Mater Sci2020;171:109203

[110] Probst P,Boulesteix AL. Tunability: importance of hyperparameters of machine learning algorithms. arXiv 2018, arXiv:1802.09596. https://doi.org/10.48550/arXiv.1802.09596. (accessed 27 May 2025)

[111] Bischl B,Lang M.Hyperparameter optimization: foundations, algorithms, best practices, and open challenges.WIREs Data Min Knowl2023;13:e1484

[112] Li L,DeSalvo G,Talwalkar A. Hyperband: a novel bandit-based approach to hyperparameter optimization. arXiv 2016, arXiv:1603.06560. https://doi.org/10.48550/arXiv.1603.06560. (accessed 27 May 2025)

[113] Victoria AH.Automatic tuning of hyperparameters using Bayesian optimization.Evol Syst2021;12:217-23

[114] Sa B,Zheng Z.High-throughput computational screening and machine learning modeling of Janus 2D III-VI van der Waals heterostructures for solar energy applications.Chem Mater2022;34:6687-701

[115] Mooraj S.A review on high-throughput development of high-entropy alloys by combinatorial methods.J Mater Inf2023;3:4

[116] Sa Z,Zhuang X.Toward high bias-stress stability P-type GaSb nanowire field-effect-transistor for gate-controlled near-infrared photodetection and photocommunication.Adv Funct Mater2023;33:2304064

[117] Kang Y,Zhang Z.Ultrahigh-performance and broadband photodetector from visible to shortwave infrared band based on GaAsSb nanowires.Chem Eng J2024;501:157392

[118] Kang Y,Zhang Z.Enhanced visible-NIR dual-band performance of GaAs nanowire photodetectors through phase manipulation.Adv Opt Mater2025:2500289

[119] Li D,Manikandan A.Ultra-fast photodetectors based on high-mobility indium gallium antimonide nanowires.Nat Commun2019;10:1664 PMCID:PMC6458123

[120] Gao Y,Hu W.First-principles computational screening of two-dimensional polar materials for photocatalytic water splitting.ACS Nano2024;18:19381-90

[121] Kangsabanik J,Taghizadeh A,Thygesen KS.Indirect band gap semiconductors for thin-film photovoltaics: high-throughput calculation of phonon-assisted absorption.J Am Chem Soc2022;144:19872-83

[122] Jiang X.High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications.J Energy Chem2021;57:351-8

[123] Tang J,Xu H.Power generation density boost of bifacial tandem solar cells revealed by high throughput optoelectrical modelling.Energy Environ Sci2024;17:6068-78

[124] Xie T.Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Phys Rev Lett2018;120:145301

[125] Liang C,Ye C,Wang B.Material symmetry recognition and property prediction accomplished by crystal capsule representation.Nat Commun2023;14:5198 PMCID:PMC10457372

[126] Mannodi-Kanakkithodi A,Sen FG,Klie RF.Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides.npj Comput Mater2020;6:296

[127] Wang H,Chen W.High-quality data enabling universality of band gap descriptor and discovery of photovoltaic perovskites.J Am Chem Soc2024;146:17636-45

[128] Kim J,Im J.Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics.npj Comput Mater2024;10:1270

[129] Mahal E,Manna SS.Machine learning-driven prediction of band-alignment types in 2D hybrid perovskites.J Mater Chem A2023;11:23547-55

[130] Nayak PK,Liu D,Ghosh D.A-cation-dependent excited state charge carrier dynamics in vacancy-ordered halide perovskites: insights from computational and machine learning models.Chem Mater2024;36:3875-85

[131] Wang S,Wu L,Zhang Q.Perovskite nanocrystals: synthesis, stability, and optoelectronic applications.Small Struct2021;2:2000124

[132] Liu J,Ye B.A review of stability-enhanced luminescent materials: fabrication and optoelectronic applications.J Mater Chem C2019;7:4934-55

[133] Liu H,Dong H.Screening stable and metastable ABO₃ perovskites using machine learning and the materials project.Comput Mater Sci2020;177:109614

[134] Burlingame Q,Loo Y.It’s time to focus on organic solar cell stability.Nat Energy2020;5:947-9

[135] Bartel CJ,Goldsmith BR.New tolerance factor to predict the stability of perovskite oxides and halides.Sci Adv2019;5:eaav0693 PMCID:PMC6368436

[136] Gu GH,Noh J,Jung Y.Perovskite synthesizability using graph neural networks.npj Comput Mater2022;8:757

[137] Fu Y,Chen J,Zhu X.Metal halide perovskite nanostructures for optoelectronic applications and the study of physical properties.Nat Rev Mater2019;4:169-88

[138] Li J,Yang X,Yang P.Review on recent progress of lead-free halide perovskites in optoelectronic applications.Nano Energy2021;80:105526

[139] Cai X,Liu J,Pan J.Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning.Mater Horiz2023;10:5288-97

[140] Liu Z,Flick AC.Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing.Joule2022;6:834-49

[141] Chen T,He S.Machine intelligence-accelerated discovery of all-natural plastic substitutes.Nat Nanotechnol2024;19:782-91 PMCID:PMC11186784

[142] Mai H,Chen D,Caruso RA.Machine learning for electrocatalyst and photocatalyst design and discovery.Chem Rev2022;122:13478-515

[143] Osman AI,Eltaweil AS.Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions.Int J Hydrogen Energy2024;67:1270-94

[144] Ma XY,Yan QB.Accelerated discovery of two-dimensional optoelectronic octahedral oxyhalides via high-throughput ab initio calculations and machine learning.J Phys Chem Lett2019;10:6734-40

[145] Jin H,Li J.Discovery of novel two-dimensional photovoltaic materials accelerated by machine learning.J Phys Chem Lett2020;11:3075-81

[146] Wang Z,Li J.Accelerated discovery of stable spinels in energy systems via machine learning.Nano Energy2021;81:105665

[147] Alibagheri E,Khazaei M,Vaez Allaei SM.Remarkable optoelectronic characteristics of synthesizable square-octagon haeckelite structures: machine learning materials discovery.Adv Funct Mater2024;34:2402390

[148] Li Y,Zhao R.Design of organic-inorganic hybrid heterostructured semiconductors via high-throughput materials screening for optoelectronic applications.J Am Chem Soc2022;144:16656-66

[149] Chen J,Zhang R.Δ-Machine learning-driven discovery of double hybrid organic-inorganic perovskites.J Mater Chem A2022;10:1402-13

[150] Chen A,Gao J.A data-driven platform for two-dimensional hybrid lead-halide perovskites.ACS Nano2023;17:13348-57

[151] Liu Y,Anker AS,Deringer VL.The amorphous state as a frontier in computational materials design.Nat Rev Mater2025;10:228-41

[152] Merchant A,Schoenholz SS,Cheon G.Scaling deep learning for materials discovery.Nature2023;624:80-5 PMCID:PMC10700131

[153] Szymanski NJ,Fei Y.An autonomous laboratory for the accelerated synthesis of novel materials.Nature2023;624:86-91 PMCID:PMC10700133

[154] Zeni C,Zügner D.A generative model for inorganic materials design.Nature2025;639:624-32 PMCID:PMC11922738

[155] Wu J,Hu M.Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells.Science2024;386:1256-64

[156] Lu JM,Guo QH.Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day.Nat Commun2024;15:8826 PMCID:PMC11470948

[157] Zhang J,Brabec CJ.Toward self-driven autonomous material and device acceleration platforms (AMADAP) for emerging photovoltaics technologies.Acc Chem Res2024;57:1434-45 PMCID:PMC11079961