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Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
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Abstract
Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor.
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“A mathematical function called CHIMERA was recently introduced to address these problems;169 it can be used with any optimizer, such as PHOENICS.”
From QSAR without borders (2020) · cites this paper as [169]
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Cites
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Research works printed but not linked: 14
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Self-citationSelf-driving laboratory for accelerated discovery of thin-film materials
MacLeod, B. P., Parlane, F. G. L., Morrissey, T. D., Häse, F., Roch, L. M., Dettelbach, K. E., Moreira, R., Yunker, L. P. E., Rooney, M. B., Deeth, J. R., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz8867 · Cited by 905
Self-citationSelf-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
Self-citationBig-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Krenn, M., Häse, F., Nigam, A., Friederich, P., & Aspuru‐Guzik, A. (2020) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000128111 · Cited by 616
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Metadata
- Authors
- Häse, F., Roch, L. M., & Aspuru-Guzik, A.
- Affiliations
- Harvard University (US)
- Department of Chemistry and Chemical Biology
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 9
- Issue
- 39
- Pages
- 7642-7655
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC6182568
- OpenAlex
- W2889272453
- Cited by
- 161
- References
- 73
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 20 · 2023: 29 · 2022: 22 · 2021: 28 · 2020: 15 · 2019: 6
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Chemical Science (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Funders
- Harvard University
- Tata Sons (A32391)
- FAS Division of Science, Harvard University
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- chimeras, multi-objective optimization, self-driving laboratories, automated experimentation, self-calibration, inverse design, interpretability
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