The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Optimizing Chemical Reactions with Deep Reinforcement Learning
Open accessAbstract
Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.
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Dermatologist-level classification of skin cancer with deep neural networks
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
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The Hitchhiker’s Guide to Flow Chemistry
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Cited by
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Bayesian reaction optimization as a tool for chemical synthesis
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Self-Driving Laboratories for Chemistry and Materials Science
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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Zahrt, A. F., Henle, J. J., Rose, B. T., Wang, Y., Darrow, W. T., & Denmark, S. E. (2019) · Science · doi:10.1126/science.aau5631 · Cited by 683
Advancing drug discovery via artificial intelligence
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654
Metadata
- Authors
- Zhou, Z., Li, X., & Zare, R. N.
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-12-15
- Volume
- 3
- Issue
- 12
- Pages
- 1337-1344
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- OpenAlex
- W2774977638
- Cited by
- 437
- References
- 8
- FWCI
- 13.44
- Type
- journal article
- Language
- en
- Open access
- diamond
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