The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
Open accessAbstract
Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high‐throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self‐driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot‐based platform can screen 2000 combinations with less than 10 mg, and machine‐learning‐enabled autonomous experimentation identifies stable compositions with less than 1 mg.
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Cites
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162
Yuan, J., Zhang, Y., Zhou, L., Zhang, G., Yip, H.-L., Lau, T.-K., Lu, X., Zhu, C., Peng, H., Johnson, P. A., et al. (2019) · Joule · doi:10.1016/j.joule.2019.01.004 · Cited by 5,883
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Accelerated search for materials with targeted properties by adaptive design
Xue, D., Balachandran, P. V., Hogden, J., Theiler, J., Xue, D., & Lookman, T. (2016) · Nature Communications · doi:10.1038/ncomms11241 · Cited by 801
Accelerating the discovery of materials for clean energy in the era of smart automation
Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., et al. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0005-z · Cited by 776
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Anderson, D. G., Levenberg, S., & Langer, R. (2004) · Nature Biotechnology · doi:10.1038/nbt981 · Cited by 747
Controlling an organic synthesis robot with machine learning to search for new reactivity
Granda, J. M., Donina, L., Dragone, V., Long, D.-L., & Cronin, L. (2018) · Nature · doi:10.1038/s41586-018-0307-8 · Cited by 740
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379
Self-citationAutonomy in materials research: a case study in carbon nanotube growth
Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R., & Maruyama, B. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.31 · Cited by 379
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V. R., Stevanovic, V., Wadia, C., Guha, S., & Buonassisi, T. (2018) · Joule · doi:10.1016/j.joule.2018.05.009 · Cited by 340
Cited by
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Renewed Prospects for Organic Photovoltaics
Zhang, G., Lin, F. R., Qi, F., Heumüller, T., Distler, A., Egelhaaf, H.-J., Li, N., Chow, P. C. Y., Brabec, C. J., Jen, A. K.-Y., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00955 · Cited by 1,052
Self-citationSelf-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Self-citationAdvantages, challenges and molecular design of different material types used in organic solar cells
Yi, J., Zhang, G., Yu, H., & Yan, H. (2023) · Nature Reviews Materials · doi:10.1038/s41578-023-00618-1 · Cited by 522
Machine learning for a sustainable energy future
Yao, Z., Lum, Y., Johnston, A., Mejia-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru-Guzik, A., Sargent, E. H., & Seh, Z. W. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00490-5 · Cited by 517
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Metadata
- Authors
- Langner, S., Häse, F., Perea, J. D., Stubhan, T., Hauch, J., Roch, L. M., Heumueller, T., Aspuru‐Guzik, A., & Brabec, C. J.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2020-02-12
- Volume
- 32
- Issue
- 14
- Pages
- e1907801-e1907801
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2972197134
- Cited by
- 258
- References
- 21
- FWCI
- 13.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Langner, S., Häse, F., Perea, J. D., Stubhan, T., Hauch, J., Roch, L. M., Heumueller, T., Aspuru‐Guzik, A., & Brabec, C. J. (2020). Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems. Advanced Materials. https://doi.org/10.1002/adma.201907801
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