Optimization of formulations using robotic experiments driven by machine learning DoE
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The role of ML in this field is likely to be data organization and hypothesis generation rather than definitive prediction.
“Therefore, the role of ML in this field is likely to be data orga nization and hypothesis generation rather than definitive prediction (Hickman et al., 2022; Cao et al., 2021).”
From Structure-function design of amaranth, buckwheat and quinoa proteins as hydrocolloids for emulsion and gel structuring (2027) · cites this paper as Cao et al., 2021
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Research works printed but not linked: 12
- Goodarzi, F., and Zendehboudi, S. (2019). A comprehensive review on emulsions and emulsion stability in chemical and energy industries. Can. J. Chem. Eng. 97, 281-309.
- Sakamoto, K., Lochhead, R.Y., Maibach, H.I., and Yamashita, Y. (2017). Cosmetic Science and Technology: Theoretical Principles and Applications (Elsevier).
- Ng, K.M., Gani, R., and Dam-Johansen, K. (2007). Chemical Product Design: Towards a Perspective through Case Studies, First Edition (Elsevier).
- Cusller, E.L., and Moggridge, G.D. (2011). Chemical Product Design, Second Edition (Cambridge University Press).
- Weissman, S.A., and Anderson, N.G. (2015). Design of experiments (DoE) and process optimization. A review of recent publications. Org. Process Res. Dev. 19, 1605-1633.
- Grangeia, H.B., Silva, C., Simo ˜es, S.P., and Reis, M.S. (2020). Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives. Eur. J. Pharm. Biopharm. 147, 19-37.
- Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182-197.
- Knowles, J. (2006). ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10, 50-66.
- Emmerich, M., Yang, K., Deutz, A., Wang, H., and Fonseca, C.M. (2016). A multicriteria generalization of Bayesian global optimization. Springer Optim. Its Appl. 107, 229-242.
- Lewis, D.D., and Gale, W.A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1994 (Association for Computing Machinery), pp. 3-12.
- Persello, C., and Bruzzone, L. (2014). Active and semisupervised learning for the classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 52, 6937-6956.
- Zhang, H. (2004). The optimality of naive Bayes. Proceedings of the 17th International Artificial Intelligence Research Society Conference (AAAI Press), pp. 562-567. https://www.cs.unb. ca/$hzhang/publications/FLAIRS04ZhangH. pdf.
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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 · see citing works
Yan, X., & Zhu, F. (2027) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2026.113137
- Authors
- Cao, L., Russo, D., Felton, K., Salley, D., Sharma, A., Keenan, G., Mauer, W., Gao, H., Cronin, L., & Lapkin, A. A.
- Venue
- Cell Reports Physical Science
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 2
- Issue
- 1
- Pages
- 100295
- ISSN
- 2666-3864
- Cited by
- 56
- References
- 0
- Language
- en
- Licence
- Funders
- BASF
- UKRI (EP/R009902/1)
- National Research Foundation
- Campus for Research Excellence and Technological Enterprise
- Cambridge Centre for Advanced Research and Education in Singapore
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