The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Two-step machine learning enables optimized nanoparticle synthesis
Open accessAbstract
In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.
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Cites
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Cited by
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
Self-citationApplication of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123
Metadata
- Authors
- Mekki-Berrada, F., Ren, Z., Huang, T., Wong, W. K., Zheng, F., Xie, J., Tian, I. P. S., Jayavelu, S., Mahfoud, Z., Bash, D., Hippalgaonkar, K., Khan, S., Buonassisi, T., Li, Q., & Wang, X.
- Venue
- npj Computational Materials
- Published
- 2021-04-20
- Volume
- 7
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W3152966503
- Cited by
- 267
- References
- 44
- FWCI
- 10.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Mekki-Berrada, F., Ren, Z., Huang, T., Wong, W. K., Zheng, F., Xie, J., Tian, I. P. S., Jayavelu, S., Mahfoud, Z., Bash, D., Hippalgaonkar, K., Khan, S., Buonassisi, T., Li, Q., & Wang, X. (2021). Two-step machine learning enables optimized nanoparticle synthesis. npj Computational Materials. https://doi.org/10.1038/s41524-021-00520-w
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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