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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
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905
Multiple Attribute Decision Making: Methods and Applications
Chen, S.-J. J., Hwang, C. L., Beckmann, M. J., & Krelle, W. E. (1981) · Medical Entomology and Zoology · Cited by 8,207
Nonlinear Multiobjective Optimization
Miettinen, K. (1998) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4615-5563-6 · Cited by 4,994
Survey of multi-objective optimization methods for engineering
Marler, R. T., & Arora, J. S. (2004) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-003-0368-6 · Cited by 4,760
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397
Das, I., & Dennis, J. E. (1998) · SIAM Journal on Optimization · doi:10.1137/s1052623496307510 · Cited by 2,677
Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003) · Evolutionary Computation · doi:10.1162/106365603321828970 · Cited by 2,601
An Overview of Evolutionary Algorithms in Multiobjective Optimization
Fonseca, C. M., & Fleming, P. J. (1995) · Evolutionary Computation · doi:10.1162/evco.1995.3.1.1 · Cited by 2,258
Multiobjective Programming and Planning
(1978) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(08)x6053-9 · Cited by 2,035
Lessons from nature about solar light harvesting
Scholes, G. D., Fleming, G. R., Olaya-Castro, A., & van Grondelle, R. (2011) · Nature Chemistry · doi:10.1038/nchem.1145 · Cited by 1,915
The weighted sum method for multi-objective optimization: new insights
Marler, R. T., & Arora, J. S. (2009) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-009-0460-7 · Cited by 1,871
Knowles, J. D. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851274 · Cited by 1,317
(1976) · Computer-Aided Design · doi:10.1016/0010-4485(76)90048-8 · Cited by 1,047
Charge and Energy Transfer Dynamics in Molecular Systems
Kühn, O., & May, V. (2023) · Wiley eBooks · doi:10.1002/9783527696277 · Cited by 987
The normalized normal constraint method for generating the Pareto frontier
Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-002-0276-1 · Cited by 767
Bentham, J. B. (2018) · Oxford University Press eBooks · doi:10.1093/oseo/instance.00219992 · Cited by 765
Adaptive weighted-sum method for bi-objective optimization: Pareto front generation
Kim, I. Y., & Weck, O. L. D. (2004) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-004-0465-1 · Cited by 722
2006 IEEE International Conference on Evolutionary Computation
(2006) · doi:10.1109/cec11288.2006 · Cited by 717
How Proteins Trigger Excitation Energy Transfer in the FMO Complex of Green Sulfur Bacteria
Adolphs, J., & Renger, T. (2006) · Biophysical Journal · doi:10.1529/biophysj.105.079483 · Cited by 696
Jailaubekov, A. E., Willard, A. P., Tritsch, J. R., Chan, W. L., Sai, N., Gearba, R. I., Kaake, L. G., Williams, K. J., Leung, K., Rossky, P. J., et al. (2012) · Nature Materials · doi:10.1038/nmat3500 · Cited by 669
Using coherence to enhance function in chemical and biophysical systems
Scholes, G. D., Fleming, G. R., Chen, L. X., Aspuru-Guzik, A., Buchleitner, A., Coker, D. F., Engel, G. S., van Grondelle, R., Ishizaki, A., Jonas, D. M., et al. (2017) · Nature · doi:10.1038/nature21425 · Cited by 668
Self-citationChlorophyll arrangement in a bacteriochlorophyll protein from Chlorobium limicola
Fenna, R. E., & Matthews, B. W. (1975) · Nature · doi:10.1038/258573a0 · Cited by 557
Synthesis of many different types of organic small molecules using one automated process
Li, J., Ballmer, S. G., Gillis, E. P., Fujii, S., Schmidt, M., Ray, A. M. E. P., Lehmann, J. W., Morehouse, G. F., & Burke, M. D. (2015) · Science · doi:10.1126/science.aaa5414 · Cited by 545
Optimizing Chemical Reactions with Deep Reinforcement Learning
Zhou, Z., Li, X., & Zare, R. N. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00492 · Cited by 462
Statistics for Experimenters: Design, Innovation and Discovery
Pigeon, J. G. (2006) · Technometrics · doi:10.1198/tech.2006.s379 · Cited by 437
Ishizaki, A., & Fleming, G. R. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3155214 · Cited by 430
Advances in Neural Information Processing Systems 26 (NIPS 2013)
Cseke, B., Opper, M., & Sanguinetti, G. (2013) · Cited by 405
Multiple objective decision making - methods and applications: A state-of-the art survey
Gál, T. (1980) · European Journal of Operational Research · doi:10.1016/0377-2217(80)90117-4 · Cited by 397
Autonomy in materials research: a case study in carbon nanotube growth
Nikolaev, P. N., Hooper, D. J., Webber, F. C., Rao, R. S., Decker, K., Krein, M. P., Poleski, J., Barto, R., & Maruyama, B. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.31 · Cited by 383
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379
Self-citationMulticriteria Optimization in Engineering and in the Sciences
Stadler, W. (1988) · Springer eBooks · doi:10.1007/978-1-4899-3734-6 · Cited by 339
Intelligent routes to the controlled synthesis of nanoparticles
Krishnadasan, S. H., Brown, R. J. C., deMello, A. J., & deMello, J. C. (2007) · Lab on a Chip · doi:10.1039/b711412e · Cited by 310
A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy
Sans, V., Porwol, L., Dragone, V., & Cronin, L. (2014) · Chemical Science · doi:10.1039/c4sc03075c · Cited by 252
Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier
Messac, A., & Mattson, C. (2004) · AIAA Journal · doi:10.2514/1.8977 · Cited by 243
Raszewski, G., & Renger, T. (2008) · Journal of the American Chemical Society · doi:10.1021/ja7099826 · Cited by 227
Fitzpatrick, D. E., Battilocchio, C., & Ley, S. V. (2015) · Organic Process Research & Development · doi:10.1021/acs.oprd.5b00313 · Cited by 207
Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals
Kitson, P. J., Marie, G., Francoïa, J., Zalesskiy, S. S., Sigerson, R. C., Mathieson, J. S., & Cronin, L. (2018) · Science · doi:10.1126/science.aao3466 · Cited by 205
Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier
Messac, A., & Mattson, C. A. (2004) · doi:10.2514/6.2004-1679 · Cited by 199
The Molecular Industrial Revolution: Automated Synthesis of Small Molecules
Trobe, M., & Burke, M. D. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201710482 · Cited by 198
Visualizing charge separation in bulk heterojunction organic solar cells
Vithanage, D. A., Devižis, A., Abramavičius, V., Infahsaeng, Y., Abramavičius, D., MacKenzie, R. C. I., Keivanidis, P. E., Yartsev, A. P., Hertel, D., Nelson, J., et al. (2013) · Nature Communications · doi:10.1038/ncomms3334 · Cited by 197
ChemOS: Orchestrating autonomous experimentation
Roch, L. M., Häse, F., Kreisbeck, C., Tamayo-Mendoza, T., Yunker, L. P. E., Hein, J. E., & Aspuru‐Guzik, A. (2018) · Science Robotics · doi:10.1126/scirobotics.aat5559 · Cited by 191
Self-citationCoherent versus Incoherent Energy Transfer and Trapping in Photosynthetic Antenna Complexes
Leegwater, J. A. (1996) · The Journal of Physical Chemistry · doi:10.1021/jp961448i · Cited by 188
Anderson, M. J., & Whitcomb, P. J. (2016) · doi:10.1201/9781315382326 · Cited by 156
Raszewski, G., Diner, B. A., Schlodder, E., & Renger, T. (2008) · Biophysical Journal · doi:10.1529/biophysj.107.123935 · Cited by 151
Moore, J. S., & Jensen, K. F. (2012) · Organic Process Research & Development · doi:10.1021/op300099x · Cited by 146
Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas
Wiecha, P. R., Arbouet, A., Girard, C., Lecestre, A., Larrieu, G., & Paillard, V. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.224 · Cited by 142
Mueller-Gritschneder, D., Graeb, H., & Schlichtmann, U. (2009) · SIAM Journal on Optimization · doi:10.1137/080729013 · Cited by 138
Tanimura, Y. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4766931 · Cited by 130
Thermo-economic optimization of Stirling heat pump by using non-dominated sorting genetic algorithm
Ahmadi, M. H., Ahmadi, M. A., Bayat, R., Ashouri, M., & Feidt, M. L. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.12.006 · Cited by 123
Modelling of oscillations in two-dimensional echo-spectra of the Fenna–Matthews–Olson complex
Hein, B., Kreisbeck, C., Kramer, T., & Rodríguez, M. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/2/023018 · Cited by 120
Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction
Picheny, V. (2014) · Statistics and Computing · doi:10.1007/s11222-014-9477-x · Cited by 112
Kreisbeck, C., Kramer, T., & Aspuru‐Guzik, A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500629s · Cited by 111
Self-citationModelling of oscillations in two-dimensional echo-spectra of the Fenna–Matthews–Olson complex
Hein, B., Kreisbeck, C., Kramer, T., & Rodrı́guez, M. (2012) · University of Regensburg Publication Server (University of Regensburg) · Cited by 107
Tanimura, Y. (2012) · Desy publication database (The Deutsches Elektronen-Synchrotron) · Cited by 103
Analysis and optimisation of packed-bed thermal reservoirs for electricity storage applications
White, A. J., McTigue, J. D., & Markides, C. N. (2016) · Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · doi:10.1177/0957650916668447 · Cited by 88
An engineering approach: Hierarchical optimization criteria
Waltz, F. M. (1967) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1967.1098537 · Cited by 80
Real-time HPLC-MS reaction progress monitoring using an automated analytical platform
Malig, T. C., Koenig, J. D. B., Situ, H., Chehal, N. K., Hultin, P. G., & Hein, J. E. (2017) · Reaction Chemistry & Engineering · doi:10.1039/c7re00026j · Cited by 74
Networking chemical robots for reaction multitasking
Caramelli, D., Salley, D., Henson, A., Aragón-Camarasa, G., Sharabi, S., Keenan, G., & Cronin, L. (2018) · Nature Communications · doi:10.1038/s41467-018-05828-8 · Cited by 71
A modified NBI and NC method for the solution of N-multiobjective optimization problems
Motta, R. D. S., Afonso, S. M. B., & Lyra, P. R. M. (2012) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-011-0729-5 · Cited by 70
Dubey, H. M., Pandit, M., & Panigrahi, B. K. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.06.039 · Cited by 70
Constrained multi-objective optimization of thermocline packed-bed thermal-energy storage
Marti, J., Geissbühler, L., Becattini, V., Haselbacher, A., & Steinfeld, A. (2018) · Applied Energy · doi:10.1016/j.apenergy.2017.12.072 · Cited by 67
Disentangling Electronic and Vibronic Coherences in Two-Dimensional Echo Spectra
Kreisbeck, C., Kramer, T., & Aspuru‐Guzik, A. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp405421d · Cited by 57
Self-citationStructure-based simulation of linear optical spectra of the CP43 core antenna of photosystem II
Müh, F., Madjet, M. E., & Renger, T. (2011) · Photosynthesis Research · doi:10.1007/s11120-011-9675-8 · Cited by 51
Jumbam, D. N., Skilton, R. A., Parrott, A. J., Bourne, R. A., & Poliakoff, M. (2012) · Journal of Flow Chemistry · doi:10.1556/jfchem.2012.00019 · Cited by 49
Tuning reaction products by constrained optimisation
Walker, B. E., Bannock, J. H., Nightingale, A. M., & de Mello, J. C. (2017) · Reaction Chemistry & Engineering · doi:10.1039/c7re00123a · Cited by 35
Optimization of thermal energy storage in packed columns
Maaliou, O., & McCoy, B. J. (1985) · Solar Energy · doi:10.1016/0038-092x(85)90090-8 · Cited by 27
Cortez, P. (2014) · Use R! · doi:10.1007/978-3-319-08263-9_6 · Cited by 24
General Subpopulation Framework and Taming the Conflict Inside Populations
Vargas, D. V., Murata, J., Takano, H., & Delbem, A. C. B. (2014) · Evolutionary Computation · doi:10.1162/evco_a_00118 · Cited by 18
Exciton Dynamics Lab for Light-Harvesting Complexes (GPU-HEOM)
Kreisbeck, C., & Kramer, T. (2017) · HubZero · doi:10.4231/d3rf5kh7g · Cited by 15
General subpopulation framework and taming the conflict inside populations
Vargas, D. V., Murata, J., Takano, H., & Delbem, A. C. B. (2015) · Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico) · Cited by 14
Robust Design of Plasmonic Waveguide Using Gradient Index and Multiobjective Optimization
Jung, J. (2015) · IEEE Photonics Technology Letters · doi:10.1109/lpt.2015.2512607 · Cited by 11
Flagged first, then by citations
1 of the 73 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
- M. Awad and R.Khanna , in Multiobjective Optimization , Apress , Berkeley, CA , 2015 , pp. 185–208
- L. M. Roch , F.Häse , C.Kreisbeck , T.Tamayo-Mendoza , L. P. E.Yunker , J. E.Hein and A.Aspuru-Guzik , chemRxiv preprint chemRxiv:5953606 , 2018
- D. Hernández-Lobato , J.Hernandez-Lobato , A.Shah and R. P.Adams , International Conference on Machine Learning , 2016 , pp. 1492–1501
- M. Emmerich and J. W.Klinkenberg , Rapport technique , Leiden University , 2008 , vol. 34
- W. Ponweiser , T.Wagner , D.Biermann and M.Vincze , International Conference on Parallel Problem Solving from Nature , 2008 , pp. 784–794
- M. Pescador-Rojas , R. H.Gómez , E.Montero , N.Rojas-Morales , M. C.Riff and C. A.Coello , International Conference on Evolutionary Multi-Criterion Optimization , 2017 , pp. 499–513
- V. Changkong and Y. Y.Haimes , North-Holland Series in System Science and Engineering , Elsevier Science Publishing Co , New York NY , 1983 , vol. 8
- O. Grodzevich and O.Romanko , Proceedings of the Fields-MITACS Industrial Problems Workshop , 2006
- M. J. Rentmeesters , W. K.Tsai and K. J.Lin , Second IEEE International Conference on Engineering of Complex Computer Systems, 1996. Proceedings. , 1996 , pp. 76–79
- Broyden, 1970, J. Applied Math., 6, 76
- R. A. Fisher , The design of experiments , Oliver and Boyd ; Edinburgh; London , 1937
- J. Snoek , H.Larochellrobotie and R. P.Adams , Advances in Neural Information Processing Systems (NIPS) , 2012 , vol. 25 , pp. 2951–2959
- J. Snoek , K.Swersky , R.Zemel and R. P.Adams , International Conference on Machine Learning , 2014 , pp. 1674–1682
- Y. Tanimura and R.Kubo ,J. Phys. Soc. Jpn., 1989 , 58 , 101114
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Self-citation
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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