Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints
Source abstract
Abstract
To enhance the power conversion efficiency (PCE) of organic photovoltaic (OPV) cells, the identification of high-performance polymer/macromolecule materials and understanding their relationship with photovoltaic performance before synthesis are critical objectives. In this study, we developed five algorithms using a dataset of 1343 experimentally validated OPV NFA acceptor materials. The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. Additionally, we explored a newly developed polymer/macromolecule structure expression, polymer-unit fingerprint ( PUFp ), which outperformed the molecular access system (MACCS) across diverse machine learning (ML) algorithms. PUFp facilitated the interpretability of structure-property relationships, enabling PCE predictions of conjugated polymers/macromolecules formed by the combination of donor (D) and acceptor (A) units. Our PUFp -ML model efficiently pre-evaluated and classified numerous acceptor materials, identifying and screening the two most promising NFA candidates. The proposed framework demonstrates the ability to design novel materials based on PUFp -ML-established feature/substructure-property relationships, providing rational design guidelines for developing high-performance OPV acceptors. These methodologies are transferable to donor materials, thereby supporting accelerated material discovery and offering insights for designing innovative OPV materials.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Organic Electronics and Photovoltaics · Machine Learning in Materials Science · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (63)
Show source-provided references
1. Wang, W., Tade, M. O. & Shao, Z. Research progress of perovskite materials in photocatalysis- and photovoltaics-related energy conversion and environmental treatment. Chem. Soc. Rev. 44, 5371–5408 (2015).10.1039/C5CS00113G ↗
2. International Energy Outlook. U.S. Energy Information Administration. (2023).
3. Bernardes, S., Lameirinhas, R. A. M., Torres, J. P. N. & Fernandes, C. A. F. Characterization and Design of Photovoltaic Solar Cells That Absorb Ultraviolet, Visible and Infrared Light. Nanomaterials (Basel) 11, 78 (2021).10.3390/nano11010078 ↗
4. Hendsbee, A. D. & Li, Y. Performance Comparisons of Polymer Semiconductors Synthesized by Direct (Hetero)Arylation Polymerization (DHAP) and Conventional Methods for Organic Thin Film Transistors and Organic Photovoltaics. Molecules 23, 1255 (2018).10.3390/molecules23061255 ↗
5. Liu, C. et al.Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine-Learning-Driven High-Throughput Platform. Adv. Mater. 36, e2300259 (2024).10.1002/adma.202300259 ↗
6. Krebs, F. C., Espinosa, N., Hosel, M., Sondergaard, R. R. & Jorgensen, M. 25th anniversary article: Rise to power-OPV-based solar parks. Adv. Mater. 26, 29–38 (2014).10.1002/adma.201302031 ↗
7. Inganas, O. Organic Photovoltaics over Three Decades. Adv. Mater. 30, e1800388 (2018).10.1002/adma.201800388 ↗
8. Sun, Y. et al. pi-Extended Nonfullerene Acceptor for Compressed Molecular Packing in Organic Solar Cells To Achieve over 20% Efficiency. J. Am. Chem. Soc. 146, 12011–12019 (2024).10.1021/jacs.4c01503 ↗
9. Chang, S.-Y., Cheng, P., Li, G. & Yang, Y. Transparent Polymer Photovoltaics for Solar Energy Harvesting and Beyond. Joule 2, 1039–1054 (2018).10.1016/j.joule.2018.04.005 ↗
10. Sun, C. et al. Heat-Insulating Multifunctional Semitransparent Polymer Solar Cells. Joule 2, 1816–1826 (2018).10.1016/j.joule.2018.06.006 ↗
11. Fan, B., Gao, H. & Jen, A. K. Biaxially Conjugated Materials for Organic Solar Cells. ACS Nano 18, 136–154 (2024).10.1021/acsnano.3c11193 ↗
12. Yang, C. et al. Optimized Crystal Framework by Asymmetric Core Isomerization in Selenium-Substituted Acceptor for Efficient Binary Organic Solar Cells. Angew. Chem. Int Ed. Engl. 62, e202313016 (2023).10.1002/anie.202313016 ↗
13. Bai, H. R. et al. Isogenous Asymmetric–Symmetric Acceptors Enable Efficient Ternary Organic Solar Cells with Thin and 300 nm Thick Active Layers Simultaneously. Adv. Funct. Mater. 32, 2200807 (2022).10.1002/adfm.202200807 ↗
14. Xu, X., Li, Y. & Peng, Q. Ternary Blend Organic Solar Cells: Understanding the Morphology from Recent Progress. Adv. Mater. 34, e2107476 (2022).10.1002/adma.202107476 ↗
15. Fu, J. et al. 19.31% binary organic solar cell and low non-radiative recombination enabled by non-monotonic intermediate state transition. Nat. Commun. 14, 1760 (2023).10.1038/s41467-023-37526-5 ↗
16. Mahmood, A. & Wang, J. L. A Review of Grazing Incidence Small- and Wide-Angle X-Ray Scattering Techniques for Exploring the Film Morphology of Organic Solar Cells. Solar RRL 4, 2000337 (2020).10.1002/solr.202000337 ↗
17. Malhotra, P., Biswas, S. & Sharma, G. D. Directed Message Passing Neural Network for Predicting Power Conversion Efficiency in Organic Solar Cells. ACS Appl. Mater. Interfaces 15, 37741–37747 (2023).10.1021/acsami.3c08068 ↗
18. Janjua, M. How Does Bridging Core Modification Alter the Photovoltaic Characteristics of Triphenylamine-Based Hole Transport Materials? Theoretical Understanding and Prediction. Chemistry 27, 4197–4210 (2021).10.1002/chem.202004299 ↗
19. Dang, M. T., Hirsch, L., Wantz, G. & Wuest, J. D. Controlling the morphology and performance of bulk heterojunctions in solar cells. Lessons learned from the benchmark poly(3-hexylthiophene):[6,6]-phenyl-C61-butyric acid methyl ester system. Chem. Rev. 113, 3734–3765 (2013).10.1021/cr300005u ↗
20. Yao, H. et al. Molecular Design of Benzodithiophene-Based Organic Photovoltaic Materials. Chem. Rev. 116, 7397–7457 (2016).10.1021/acs.chemrev.6b00176 ↗
21. Hu, H. et al. Design of Donor Polymers with Strong Temperature-Dependent Aggregation Property for Efficient Organic Photovoltaics. Acc. Chem. Res. 50, 2519–2528 (2017).10.1021/acs.accounts.7b00293 ↗
22. Wang, Z., Zhu, L., Shuai, Z. & Wei, Z. A-π-D-π-A Electron-Donating Small Molecules for Solution-Processed Organic Solar Cells: A Review. Macromol. Rapid Commun. 38, 1700470 (2017).10.1002/marc.201700470 ↗
23. Hou, J., Inganäs, O., Friend, R. H. & Gao, F. Organic solar cells based on non-fullerene acceptors. Nat. Mater. 17, 119–128 (2018).10.1038/nmat5063 ↗
24. Yan, C. et al. Non-fullerene acceptors for organic solar cells. Nat. Rev. Mater. 3, 18003 (2018).10.1038/natrevmats.2018.3 ↗
25. Yao, H. et al. 14.7% Efficiency Organic Photovoltaic Cells Enabled by Active Materials with a Large Electrostatic Potential Difference. J. Am. Chem. Soc. 141, 7743–7750 (2019).10.1021/jacs.8b12937 ↗
26. Zhang, Q. et al. High-Efficiency Non-Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry. Adv. Sci. (Weinh.) 9, e2104742 (2022).
27. Ren, Y., Li, M. Y., Sui, M. Y., Sun, G. Y. & Su, Z. M. Energy differences as descriptors for the correlation between J(SC) and V(OC) in nonfullerene organic photovoltaics. Chem. Commun. 59, 7212–7215 (2023).10.1039/D3CC01904G ↗
28. Sun, R. et al. Single-Junction Organic Solar Cells with 19.17% Efficiency Enabled by Introducing One Asymmetric Guest Acceptor. Adv. Mater. 34, e2110147 (2022).10.1002/adma.202110147 ↗
29. Monteiro-de-Castro, G. & Borges, I. A. Jr Hammett's analysis of the substituent effect in functionalized diketopyrrolopyrrole (DPP) systems: Optoelectronic properties and intramolecular charge transfer effects. J. Comput. Chem. 44, 2256–2273 (2023).10.1002/jcc.27195 ↗
30. He, C. et al. Manipulating the D:A interfacial energetics and intermolecular packing for 19.2% efficiency organic photovoltaics. Energy Environ. Sci. 15, 2537–2544 (2022).10.1039/D2EE00595F ↗
31. Cao, J. & Xu, Z. Providing a Photovoltaic Performance Enhancement Relationship from Binary to Ternary Polymer Solar Cells via. Machine Learning. Polymers 16, 1496 (2024).
32. Zheng, Z. et al. Tandem Organic Solar Cell with 20.2% Efficiency. Joule 6, 171–184 (2022).10.1016/j.joule.2021.12.017 ↗
33. Rodríguez-Martínez, X., Pascual-San-José, E. & Campoy-Quiles, M. Accelerating organic solar cell material’s discovery: high-throughput screening and big data. Energy Environ. Sci. 14, 3301–3322 (2021).10.1039/D1EE00559F ↗
34. Liu, C. Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine-Learning-Driven High-Throughput Platform. Adv. Mater. 36, e2300259 (2023).10.1002/adma.202300259 ↗
35. Bhat, V., Callaway, C. P. & Risko, C. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials. Chem. Rev. 123, 7498–7547 (2023).10.1021/acs.chemrev.2c00704 ↗
36. Zhang, G. et al. Nonfullerene Acceptor Molecules for Bulk Heterojunction Organic Solar Cells. Chem. Rev. 118, 3447–3507 (2018).10.1021/acs.chemrev.7b00535 ↗
37. Wu, Y., Guo, J., Sun, R. & Min, J. Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells. npj Comput. Mater 6, 120 (2020).10.1038/s41524-020-00388-2 ↗
38. Sun, W. et al. Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials. J. Phys. Chem. Lett. 12, 8847–8854 (2021).10.1021/acs.jpclett.1c02554 ↗
39. Lee, M. H. Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n‐Type Organic Field‐Effect Transistors. Adv. Electron. Mater. 5, 1900573 (2019).10.1002/aelm.201900573 ↗
40. Jørgensen, P. B. et al. Machine learning-based screening of complex molecules for polymer solar cells. J. Chem. Phys. 148, 241735 (2018).10.1063/1.5023563 ↗
41. Liu, L. et al. The MatHub-3d first-principles repository and the applications on thermoelectrics. Mater. Genome Eng. Adv. 2, e21 (2024).10.1002/mgea.21 ↗
42. Zhang, X. et al. Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials. J. Chem. Theory Comput. 20, 2908–2920 (2024).10.1021/acs.jctc.3c01385 ↗
43. Huang, X. & Ju, S. Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity. J. Appl. Phys 135, 171101 (2024).10.1063/5.0201522 ↗
44. Mannan, S., Bihani, V., Krishnan, N. M. A. & Mauro, J. C. Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning. Mater. Genome Eng. Adv. 2, e25 (2024).10.1002/mgea.25 ↗
45. Mahmood, A., Irfan, A. & Wang, J.-L. Machine Learning for Organic Photovoltaic Polymers: A Minireview. Chin. J. Polym. Sci. 40, 870–876 (2022).10.1007/s10118-022-2782-5 ↗
46. Nagasawa, S., Al-Naamani, E. & Saeki, A. Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest. J. Phys. Chem. Lett. 9, 2639–2646 (2018).10.1021/acs.jpclett.8b00635 ↗
47. Sahu, H., Rao, W., Troisi, A. & Ma, H. Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors. Adv. Energy Mater. 8, 1801032 (2018).10.1002/aenm.201801032 ↗
48. Mahmood, A., Sandali, Y. & Wang, J. L. Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning. Phys. Chem. Chem. Phys. 25, 10417–10426 (2023).10.1039/D3CP00177F ↗
49. Suthar, R., Abhijith, T., Sharma, P. & Karak, S. Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells. Solar Energy 250, 119–127 (2023).10.1016/j.solener.2022.12.029 ↗
50. Mahmood, A. & Wang, J.-L. Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ. Sci. 14, 90–105 (2021).10.1039/D0EE02838J ↗
51. Suthar, R., T, A. & Karak, S. Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors. J. Mater. Chem. A 11, 22248–22258 (2023).10.1039/D3TA04603F ↗
52. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).10.1038/nature14539 ↗
53. Wang, H. et al. Efficient screening framework for organic solar cells with deep learning and ensemble learning. npj Comput. Mater. 9, 200 (2023).10.1038/s41524-023-01155-9 ↗
54. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rule. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).10.1021/ci00057a005 ↗
55. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model 50, 742–754 (2010).10.1021/ci100050t ↗
56. Consonni, R. T. V. Handbook-of-molecular-descriptors. Methods and principles in medicinal chemistry (2000).
57. Zhang, X. et al. Polymer-Unit Fingerprint (PUFp): An Accessible Expression of Polymer Organic Semiconductors for Machine Learning. ACS Appl. Mater. Interfaces 15, 21537–21548 (2023).10.1021/acsami.3c03298 ↗
58. Wiener, A. L. M. C. Classification and Regression by randomForest. (2007).
59. Valkenborg, D., Rousseau, A. J., Geubbelmans, M. & Burzykowski, T. Support vector machines. Am. J. Orthod. Dentofac. Orthop. 164, 754–757 (2023).10.1016/j.ajodo.2023.08.003 ↗
60. Padula, D. & Troisi, A. Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning. Adv. Energy Mater. 9, 1902463 (2019).10.1002/aenm.201902463 ↗
61. Zhao, Z.-W., del Cueto, M., Geng, Y. & Troisi, A. Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells. Chem. Mater. 32, 7777–7787 (2020).10.1021/acs.chemmater.0c02325 ↗
62. Polman, A., Knight, M., Garnett, E. C., Ehrler, B. & Sinke, W. C. Photovoltaic materials: Present efficiencies and future challenges. Science 352, aad4424 (2016).10.1126/science.aad4424 ↗
63. Graetzel, M., Janssen, R. A., Mitzi, D. B. & Sargent, E. H. Materials interface engineering for solution-processed photovoltaics. Nature 488, 304–312 (2012).10.1038/nature11476 ↗