Construction frontier molecular orbital prediction model with transfer learning for organic materials

Chow, P. C. Y. & Someya, T. Organic Photodetectors for Next-Generation Wearable Electronics. Adv. Mater. 32, 1902045 (2020).Article 
CAS 

Google Scholar 
Salehi, A., Fu, X., Shin, D. & So, F. Recent Advances in OLED Optical Design. Adv. Funct. Mater. 29, 1808803 (2019).Article 

Google Scholar 
Cao, W. & Xue, J. Recent progress in organic photovoltaics: device architecture and optical design. Energy Environ. Sci. 7, 2123–2144 (2014).Article 
CAS 

Google Scholar 
Geerlings, P., De Proft, F. & Langenaeker, W. Conceptual Density Functional Theory. Chem. Rev. 103, 1793–1874 (2003).Article 
CAS 
PubMed 

Google Scholar 
Sato, N., Seki, K. & Inokuchi, H. Polarization energies of organic solids determined by ultraviolet photoelectron spectroscopy. J. Chem. Soc. Faraday Trans. 2 Mol. Chem. Phys. 77, 1621–1633 (1981).CAS 

Google Scholar 
Arantes, C. et al. Comparative analysis of the energy levels of planar and core-twisted perylene bisimides in solution and solid state by UV/VIS, CV, and UPS/IPES. Appl. Phys. A 108, 629–637 (2012).Article 
CAS 

Google Scholar 
Traving, M. et al. Electronic structure of WSe 2: A combined photoemission and inverse photoemission study. Phys. Rev. B 55, 10392–10399 (1997).Article 
CAS 

Google Scholar 
Pluczyk, S., Vasylieva, M. & Data, P. Using Cyclic Voltammetry, UV-Vis-NIR, and EPR Spectroelectrochemistry to Analyze Organic Compounds. JoVE J. Vis. Exp. 140, e56656 (2018).
Google Scholar 
Yadav, R. A. K., Dubey, D. K., Chen, S.-Z., Liang, T.-W. & Jou, J.-H. Role of Molecular Orbital Energy Levels in OLED Performance. Sci. Rep. 10, 9915 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shan, T., Hou, X., Yin, X. & Guo, X. Organic photodiodes: device engineering and applications. Front. Optoelectron. 15, 49 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Kondolot Solak, E. & Irmak, E. Advances in organic photovoltaic cells: a comprehensive review of materials, technologies, and performance. RSC Adv. 13, 12244–12269 (2023).Article 

Google Scholar 
Ahmad, V. et al. Charge and exciton dynamics of OLEDs under high voltage nanosecond pulse: towards injection lasing. Nat. Commun. 11, 4310 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, X. et al. Evolution Between Exciton and Exciplex Emission in Planar Heterojunction OLEDs with Different Hole-Injection Characteristics. Phys. Rev. Appl. 19, 054067 (2023).Article 
CAS 

Google Scholar 
Cui, Y. et al. Single‐Junction Organic Photovoltaic Cell with 19% Efficiency. Adv. Mater. 33, 2102420 (2021).Article 
CAS 

Google Scholar 
Zhang, F. et al. Suppressed carrier recombination and boosted open-circuit voltage of indoor organic photovoltaic device with fullerene acceptor. Appl. Phys. A 129, 621 (2023).Article 
CAS 

Google Scholar 
Zhang, Z., Ding, Z., Dou, C., Liu, J. & Wang, L. Development of a donor polymer using a B ← N unit for suitable LUMO/HOMO energy levels and improved photovoltaic performance. Polym. Chem. 6, 8029–8035 (2015).Article 
CAS 

Google Scholar 
Song, S., Vuckovic, S., Sim, E. & Burke, K. Density-Corrected DFT Explained: Questions and Answers. J. Chem. Theory Comput. 18, 817–827 (2022).Article 
CAS 
PubMed 

Google Scholar 
Blankevoort, N. et al. Exploring the Impact of the HOMO–LUMO Gap on Molecular Thermoelectric Properties: A Comparative Study of Conjugated Aromatic, Quinoidal, and Donor–Acceptor Core Systems. ACS Omega 9, 8471–8477 (2024).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kobayashi, Y., Miyake, Y., Ishiwari, F., Ishiwata, S. & Saeki, A. Machine learning of atomic force microscopy images of organic solar cells. RSC Adv. 13, 15107–15113 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kötz, E. R., Neff, H. & Müller, K. A UPS, XPS and work function study of emersed silver, platinum and gold electrodes. J. Electroanal. Chem. Interfacial Electrochem. 215, 331–344 (1986).Article 

Google Scholar 
Yoshida, H. Principle and application of low energy inverse photoemission spectroscopy: A new method for measuring unoccupied states of organic semiconductors. J. Electron Spectrosc. Relat. Phenom. 204, 116–124 (2015).Article 
CAS 

Google Scholar 
Liu, Y., Zhao, T., Ju, W. & Shi, S. Materials discovery and design using machine learning. J. Materiomics 3, 159–177 (2017).Article 

Google Scholar 
Abroshan, H. et al. Machine Learning for the Design of Novel OLED Materials. In Machine Learning in Materials Informatics: Methods and Applications 1416, 33–49 (American Chemical Society, 2022).Anabestani, H., Nabavi, S. & Bhadra, S. Advances in Flexible Organic Photodetectors: Materials and Applications. Nanomaterials 12, 3775 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, Z.-W., Geng, Y., Troisi, A. & Ma, H. Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics. Adv. Intell. Syst. 4, 2100261 (2022).Article 

Google Scholar 
Jorner, K., Tomberg, A., Bauer, C., Sköld, C. & Norrby, P.-O. Organic reactivity from mechanism to machine learning. Nat. Rev. Chem. 5, 240–255 (2021).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 

Google Scholar 
Moosavi, S. M., Jablonka, K. M. & Smit, B. The Role of Machine Learning in the Understanding and Design of Materials. J. Am. Chem. Soc. 142, 20273–20287 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Braham, E. J., Davidson, R. D., Al-Hashimi, M., Arróyave, R. & Banerjee, S. Navigating the design space of inorganic materials synthesis using statistical methods and machine learning. Dalton Trans. 49, 11480–11488 (2020).Article 
CAS 
PubMed 

Google Scholar 
Takahashi, K. & Tanaka, Y. Material synthesis and design from first principle calculations and machine learning. Comput. Mater. Sci. 112, 364–367 (2016).Article 
CAS 

Google Scholar 
Wang, S. et al. Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach. J. Comput. Chem. 45, 321–330 (2024).Article 
CAS 
PubMed 

Google Scholar 
Li, P., Wang, Z., Li, W., Yuan, J. & Chen, R. Design of Thermally Activated Delayed Fluorescence Materials with High Intersystem Crossing Efficiencies by Machine Learning-Assisted Virtual Screening. J. Phys. Chem. Lett. 13, 9910–9918 (2022).Article 
CAS 
PubMed 

Google Scholar 
Alzahrani, F. M. A. et al. Machine learning assisted prediction of band gaps and designing of new polymers for photodetectors: A complete pipeline. Comput. Mater. Sci. 239, 112961 (2024).Article 
CAS 

Google Scholar 
Alfryyan, N. et al. Designing of near-IR organic semiconductors for photodetectors: Machine learning and data mining assisted efficient pipeline. Mater. Today Commun. 36, 106556 (2023).Article 
CAS 

Google Scholar 
Wang, H. et al. Efficient screening framework for organic solar cells with deep learning and ensemble learning. Npj Comput. Mater. 9, 1–11 (2023).Article 

Google Scholar 
Huang, D. et al. A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells. Chem. Eng. J. 475, 145958 (2023).Article 
CAS 

Google Scholar 
Wang, K. et al. Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells. Mol. Syst. Des. Eng. 8, 799–809 (2023).Article 
CAS 

Google Scholar 
Huang, D. et al. Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning. Polymers 15, 2954 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lopez, S. A. et al. The Harvard organic photovoltaic dataset. Sci. Data 3, 160086 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Justin, K. & Frederick, P. R. Chemical substructures that enrich for biological activity. Bioinformatics 24, 2518–2525 (2008).Article 

Google Scholar 
Hall, L. H. & Kier, L. B. Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information. J. Chem. Inf. Comput. Sci. 35, 1039–1045 (1995).Article 
CAS 

Google Scholar 
Cereto-Massagué, A. et al. Molecular fingerprint similarity search in virtual screening. Methods 71, 58–63 (2015).Article 
PubMed 

Google Scholar 
Hert, J., Irwin, J. J., Laggner, C., Keiser, M. J. & Shoichet, B. K. Quantifying biogenic bias in screening libraries. Nat. Chem. Biol. 5, 479–483 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Landrum, G. RDKit Documentation.Zhuang, F. et al. A Comprehensive Survey on Transfer Learning. Proc. IEEE 109, 43–76 (2021).Article 

Google Scholar 
Mennucci, B. Polarizable continuum model. WIREs Comput. Mol. Sci. 2, 386–404 (2012).Article 
CAS 

Google Scholar 
Tao, M., Peng, L. & Gu, F. L. Theoretical studies on the effects of π-bridge engineering on the photoelectric performance of Y6. N. J. Chem. 46, 19963–19974 (2022).Article 
CAS 

Google Scholar 
Riley, K. E. et al. Gaussian 09, Revision C.01, Gaussian, Inc., Wallingford CT, 2010.Liu, Z., Lu, T. & Chen, Q. Intermolecular interaction characteristics of the all-carboatomic ring, cyclo[18]carbon: Focusing on molecular adsorption and stacking. Carbon 171, 514–523 (2021).Article 
CAS 

Google Scholar 
Ali, Z. A., Abduljabbar, Z. H., Taher, H. A., Sallow, A. B. & Almufti, S. M. Exploring the Power of eXtreme Gradient Boosting Algorithm in Machine Learning: a Review. Acad. J. Nawroz Univ. 12, 320–334 (2023).Article 

Google Scholar 
Transfer learning for solvation free energies. From quantum chemistry to experiments. Chem. Eng. J. 418, 129307 (2021).Article 

Google Scholar 
Nakata, M. & Shimazaki, T. PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry. J. Chem. Inf. Model. 57, 1300–1308 (2017).Article 
CAS 
PubMed 

Google Scholar 
Mangalathu, S., Hwang, S.-H. & Jeon, J.-S. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 219, 110927 (2020).Article 

Google Scholar 
Yadav, S., Mittal, P. & Negi, S. Recent advancements over a decade for organic light-emitting diodes: from structural diversity, role of layers, colour emission, material classification, performance improvement, fabrication to applications. Bull. Mater. Sci. 45, 1–26 (2022).Article 

Google Scholar 
Zhang, J. & Lu, T. Efficient evaluation of electrostatic potential with computerized optimized code. Phys. Chem. Chem. Phys. 23, 20323–20328 (2021).Article 
CAS 
PubMed 

Google Scholar 
Hundemer, F. et al. Acceptor Derivatization of the 4CzIPN TADF System: Color Tuning and Introduction of Functional Groups. Chem. Open 8, 1413–1420 (2019).CAS 

Google Scholar 
Hong, X. et al. TADF molecules with π-extended acceptors for simplified high-efficiency blue and white organic light-emitting diodes. Chem 8, 1705–1719 (2022).Article 
CAS 

Google Scholar 
Hasan, M. et al. Probing polaron-induced exciton quenching in TADF based organic light-emitting diodes. Nat. Commun. 13, 254 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, J., Yao, C., Ali, M. U., Miao, J. & Meng, H. Recent advances in high-performance organic solar cells enabled by acceptor–donor–acceptor–donor–acceptor (A–DA′D–A) type acceptors. Mater. Chem. Front. 4, 3487–3504 (2020).Article 
CAS 

Google Scholar 
Yuan, J. et al. Single-Junction Organic Solar Cell with over 15% Efficiency Using Fused-Ring Acceptor with Electron-Deficient Core. Joule 3, 1140–1151 (2019).Article 
CAS 

Google Scholar 
Lopez, S. A., Sanchez-Lengeling, B., de Goes Soares, J. & Aspuru-Guzik, A. Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics. Joule 1, 857–870 (2017).Article 
CAS 

Google Scholar 
Wang, D., Huang, S., Wang, C., Yue, Y. & Zhang, Q. Computational prediction for oxidation and reduction potentials of organic molecules used in organic light-emitting diodes. Org. Electron. 64, 216–222 (2019).Article 
CAS 

Google Scholar 
Bhat, G. et al. Energy-Level Manipulation in Novel Indacenodithiophene-Based Donor–Acceptor Polymers for Near-Infrared Organic Photodetectors. ACS Appl. Mater. Interfaces 13, 29866–29875 (2021).Article 
CAS 
PubMed 

Google Scholar 
Lv, L. et al. Significant enhancement of responsivity of organic photodetectors upon molecular engineering. J. Mater. Chem. C. 7, 5739–5747 (2019).Article 
CAS 

Google Scholar 
Ha, J.-W. et al. Effect of Cyano Substitution on Non-Fullerene Acceptor for Near-Infrared Organic Photodetectors above 1000 nm. Adv. Funct. Mater. 33, 2211486 (2023).Article 
CAS 

Google Scholar 
Choi, T. et al. Effect of Organic Molecular Volume on Organic Photodiodes. Adv. Opt. Mater. 11, 2300087 (2023).Article 
CAS 

Google Scholar