The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
Open accessAbstract
Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Bulk heterojunction solar cells with internal quantum efficiency approaching 100%
Park, S. H., Roy, A., Beaupré, S., Cho, S., Coates, N., Moon, J. S., Moses, D., Leclerc, M., Lee, K., & Heeger, A. J. (2009) · Nature Photonics · doi:10.1038/nphoton.2009.69 · Cited by 4,073
On the Experimental Attainment of Optimum Conditions
Box, G. E. P., & Wilson, K. B. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_23 · Cited by 3,448
Non-fullerene acceptors for organic solar cells
Yan, C., Barlow, S., Wang, Z., Yan, H., Jen, A. K.-Y., Marder, S. R., & Zhan, X. (2018) · Nature Reviews Materials · doi:10.1038/natrevmats.2018.3 · Cited by 2,710
Time series analysis forecasting and control
Janacek, G. (2010) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2009.00643.x · Cited by 2,584
Recent Advances in Bulk Heterojunction Polymer Solar Cells
Lu, L., Zheng, T., Wu, Q., Schneider, A. M., Zhao, D., & Yu, L. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00098 · Cited by 2,550
Box, G. E. P. (1976) · Journal of the American Statistical Association · doi:10.1080/01621459.1976.10480949 · Cited by 2,114
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Next-generation organic photovoltaics based on non-fullerene acceptors
Cheng, P., Li, G., Zhan, X., & Yang, Y. (2018) · Nature Photonics · doi:10.1038/s41566-018-0104-9 · Cited by 1,905
Cited by
Self-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
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Autonomous experimentation systems for materials development: A community perspective
Stach, E., DeCost, B., Kusne, A. G., Hattrick-Simpers, J., Brown, K. A., Reyes, K. G., Schrier, J., Billinge, S., Buonassisi, T., Foster, I., et al. (2021) · Matter · doi:10.1016/j.matt.2021.06.036 · Cited by 398
Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344
Machine Learning in Nanoscience: Big Data at Small Scales
Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D., & Celano, U. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04090 · Cited by 229
Metadata
- Authors
- Cao, B., Adutwum, L. A., Oliynyk, A. O., Luber, E. J., Olsen, B. C., Mar, A., & Buriak, J. M.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-07-20
- Volume
- 12
- Issue
- 8
- Pages
- 7434-7444
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2884258597
- Cited by
- 316
- References
- 59
- FWCI
- 11.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics. https://app.cochat.ai/papers/10.1021/acsnano.8b04726
- The paper
- Cao, B., Adutwum, L. A., Oliynyk, A. O., Luber, E. J., Olsen, B. C., Mar, A., & Buriak, J. M. (2018). How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics. ACS Nano. https://doi.org/10.1021/acsnano.8b04726
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
