Bayesian reaction optimization as a tool for chemical synthesis

Author:

Shields Benjamin J.,Stevens JasonORCID,Li JunORCID,Parasram Marvin,Damani Farhan,Alvarado Jesus I. Martinez,Janey Jacob M.ORCID,Adams Ryan P.ORCID,Doyle Abigail G.ORCID

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference63 articles.

1. Carlson, R. Design and Optimization in Organic Synthesis (Elsevier, 1992).

2. Luo, G. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Netw. Model. Anal. Health Inform. Bioinform. 5, 18 (2016).

3. Snoek, J., Larochelle, H. & Adams, R. P. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems Vol. 25 (eds Pereira, F. et al.) 2951–2959 (Curran Associates Inc., 2012).

4. Häse, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. Phoenics: a Bayesian Optimizer for Chemistry. ACS Cent. Sci. 4, 1134–1145 (2018).

5. Griffiths, R.-R. & Hernández-Lobato, J. M. Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chem. Sci. 11, 577–586 (2020).

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