Self-optimisation and model-based design of experiments for developing a C–H activation flow process

Scholarly Works

• Rogers, A. W.; Lane, A.; Mendoza, C.; Watson, S.; Kowalski, A.; Martin, P.; Zhang, D. Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development. Chemical Engineering Science 2024, 300, 120580. doi:10.1016/j.ces.2024.120580
• Tom, G.; Schmid, S. P.; Baird, S. G.; Cao, Y.; Darvish, K.; Hao, H.; Lo, S.; Pablo-García, S.; Rajaonson, E. M.; Skreta, M.; Yoshikawa, N.; Corapi, S.; Akkoc, G. D.; Strieth-Kalthoff, F.; Seifrid, M.; Aspuru-Guzik, A. Self-Driving Laboratories for Chemistry and Materials Science. Chemical reviews 2024, 124, 9633–9732. doi:10.1021/acs.chemrev.4c00055
• Agunloye, E.; Petsagkourakis, P.; Yusuf, M.; Labes, R.; Chamberlain, T.; Muller, F. L.; Bourne, R. A.; Galvanin, F. Automated kinetic model identification via cloud services using model-based design of experiments. Reaction Chemistry & Engineering 2024, 9, 1859–1876. doi:10.1039/d4re00047a
• Liu, P.; Jin, H.; Chen, Y.; Wang, D.; Yan, H.; Wu, M.; Zhao, F.; Zhu, W. Process analytical technologies and self-optimization algorithms in automated pharmaceutical continuous manufacturing. Chinese Chemical Letters 2024, 35, 108877. doi:10.1016/j.cclet.2023.108877
• Wang, J. Y.; Stevens, J. M.; Kariofillis, S. K.; Tom, M.-J.; Golden, D. L.; Li, J.; Tabora, J. E.; Parasram, M.; Shields, B. J.; Primer, D. N.; Hao, B.; Del Valle, D.; DiSomma, S.; Furman, A.; Zipp, G. G.; Melnikov, S.; Paulson, J.; Doyle, A. G. Identifying general reaction conditions by bandit optimization. Nature 2024, 626, 1025–1033. doi:10.1038/s41586-024-07021-y
• Parveen, F.; Morris, H. J.; West, H.; Slater, A. G. Continuous flow synthesis of meso-substituted porphyrins with inline UV–Vis analysis. Journal of Flow Chemistry 2024, 14, 23–31. doi:10.1007/s41981-023-00305-w
• Rogers, A. W.; Lane, A.; Mendoza, C.; Watson, S.; Kowalski, A.; Martin, P.; Zhang, D. Integrating Knowledge-Guided Symbolic Regression and Model-Based Design of Experiments to Accelerate Process Flow Diagram Development. IFAC-PapersOnLine 2024, 58, 127–132. doi:10.1016/j.ifacol.2024.08.325
• Shen, R.; Su, W. A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products. Pharmaceutical Fronts 2023, 5, e219–e226. doi:10.1055/s-0043-1777425
• Liang, R.; Hu, H.; Han, Y.; Chen, B.; Yuan, Z. CAPBO: A cost‐aware parallelized Bayesian optimization method for chemical reaction optimization. AIChE Journal 2023, 70. doi:10.1002/aic.18316
• Pankajakshan, A.; Bawa, S. G.; Gavriilidis, A.; Galvanin, F. Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study. Reaction Chemistry & Engineering 2023, 8, 3000–3017. doi:10.1039/d3re00156c
• Lin, D.-Z.; Fang, G.; Liao, K. Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis. Challenges and Advances in Computational Chemistry and Physics; Springer International Publishing, 2023; pp 227–275. doi:10.1007/978-3-031-37196-7_8
• Cenci, F.; Pankajakshan, A.; Facco, P.; Galvanin, F. An exploratory model-based design of experiments approach to aid parameters identification and reduce model prediction uncertainty. Computers & Chemical Engineering 2023, 177, 108353. doi:10.1016/j.compchemeng.2023.108353
• Filipa de Almeida, A.; Rodrigues, T. doi:10.1002/9781119855668.ch10
• Taylor, C. J.; Pomberger, A.; Felton, K. C.; Grainger, R.; Barecka, M.; Chamberlain, T. W.; Bourne, R. A.; Johnson, C. N.; Lapkin, A. A. A Brief Introduction to Chemical Reaction Optimization. Chemical reviews 2023, 123, 3089–3126. doi:10.1021/acs.chemrev.2c00798
• Babu, S. A.; Padmavathi, R.; Aggarwal, Y.; Kaur, R.; Suwasia, S. doi:10.1002/9783527834242.chf0006
• Knoll, S.; Jusner, C. E.; Sagmeister, P.; Williams, J. D.; Hone, C. A.; Horn, M.; Kappe, C. O. Autonomous model-based experimental design for rapid reaction development. Reaction Chemistry & Engineering 2022, 7, 2375–2384. doi:10.1039/d2re00208f
• van der Westhuizen, C. J.; du Toit, J.; Neyt, N.; Riley, D.; Panayides, J.-L. Use of open-source software platform to develop dashboards for control and automation of flow chemistry equipment. Digital Discovery 2022, 1, 596–604. doi:10.1039/d2dd00036a
• Rincón, J. A.; Nieves‐Remacha, M. J.; Mateos, C. doi:10.1002/9783527824595.ch2
• Rodriguez-Zubiri, M.; Felpin, F.-X. Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?. Organic Process Research & Development 2022, 26, 1766–1793. doi:10.1021/acs.oprd.2c00102
• Bennett, J. A.; Abolhasani, M. Autonomous chemical science and engineering enabled by self-driving laboratories. Current Opinion in Chemical Engineering 2022, 36, 100831. doi:10.1016/j.coche.2022.100831

Explore citations to this article at