Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm
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Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.
“Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [118]
Cites
Klier, K. (1982) · Advances in catalysis · doi:10.1016/s0360-0564(08)60455-1 · Cited by 760 · see citing works
Herman, R. G. (1979) · Journal of Catalysis · doi:10.1016/0021-9517(79)90132-5 · Cited by 579 · see citing works
The activity and state of the copper surface in methanol synthesis catalysts
Chinchen, G. C., Waugh, K. C., & Whan, D. A. (1986) · Applied Catalysis · doi:10.1016/s0166-9834(00)81226-9 · Cited by 555 · see citing works
Distance Measurements in the Nanometer Range by Pulse EPR
Jeschke, G. (2002) · ChemPhysChem · doi:10.1002/1439-7641(20021115)3:11 · Cited by 292 · see citing works
An evolutionary approach in the combinatorial selection and optimization of catalytic materials
Wolf, D., Buyevskaya, O. V., & Baerns, M. (2000) · Applied Catalysis A General · doi:10.1016/s0926-860x(00)00643-8 · Cited by 210 · see citing works
Shephard, D. S., Zhou, W., Maschmeyer, T., Matters, J. M., Roper, C. L., Parsons, S., Johnson, B. F. G., & Duer, M. J. (1998) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19981016)37:19 · Cited by 166 · see citing works
Sun, Q., Zhang, Y., Chen, H.-Y., Deng, J., Wu, D. Q., & Chen, S. (1997) · Journal of Catalysis · doi:10.1006/jcat.1997.1554 · Cited by 160 · see citing works
Applications of combinatorial methods in catalysis
Hagemeyer, A., Jandeleit, B., Liu, Y., et al. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00886-9 · Cited by 152 · see citing works
Jun, K., Shen, W., Rao, K. S. R., et al. (1998) · Applied Catalysis A General · doi:10.1016/s0926-860x(98)00195-1 · Cited by 129 · see citing works
Heinz, U., Hegetschweiler, Κ., Acklin, P., Faller, B., Lattmann, R., & Schnebli, H. P. (1999) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19990903)38:17 · Cited by 128 · see citing works
The structure of the copper/zinc oxide catalyst by an in-situ EXAFS study
Tohji, K., Udagawa, Y., Mizushima, T., et al. (1985) · The Journal of Physical Chemistry · doi:10.1021/j100272a019 · Cited by 110 · see citing works
Buyevskaya, O. V., Brückner, A., Kondratenko, E. V., et al. (2001) · Catalysis Today · doi:10.1016/s0920-5861(01)00329-7 · Cited by 108 · see citing works
Neural network as a tool for catalyst development
Hattori, T., & Kito, S. (1995) · Catalysis Today · doi:10.1016/0920-5861(94)00148-u · Cited by 107 · see citing works
Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling
Huang, K., Chen, F., & Lü, D.-W. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00659-7 · Cited by 104 · see citing works
Ethylene and propene by oxidative dehydrogenation of ethane and propane
Buyevskaya, O. V., Wolf, D., & Baerns, M. (2000) · Catalysis Today · doi:10.1016/s0920-5861(00)00411-9 · Cited by 99 · see citing works
Huang, K., Zhan, X., Chen, F., & Lü, D.-W. (2002) · Chemical Engineering Science · doi:10.1016/s0009-2509(02)00432-3 · Cited by 96 · see citing works
Combinatorial preparation and high-throughput catalytic tests of multi-component deNOx catalysts
Richter, M. (2002) · Applied Catalysis B: Environmental · doi:10.1016/s0926-3373(01)00290-9 · Cited by 91 · see citing works
Feedforward neural networks in catalysis
Holeňa, M., & Baerns, M. (2003) · Catalysis Today · doi:10.1016/s0920-5861(03)00147-0 · Cited by 77 · see citing works
Artificial neural network aided design of catalyst for propane ammoxidation
Hou, Z., Dai, Q., Wu, X., et al. (1997) · Applied Catalysis A General · doi:10.1016/s0926-860x(97)00063-x · Cited by 75 · see citing works
Kiener, C. A., Kurtz, M., Wilmer, H., et al. (2003) · Journal of Catalysis · doi:10.1016/s0021-9517(02)00134-3 · Cited by 71 · see citing works
Hunting for better catalysts and materials-combinatorial chemistry and high throughput technology
Scheidtmann, J., Weiss, P. W., & Maier, W. F. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00831-6 · Cited by 70 · see citing works
High-throughput synthesis and screening of catalytic materials
Rodemerck, U., Wolf, D., Buyevskaya, O. V., et al. (2001) · Chemical Engineering Journal · doi:10.1016/s1385-8947(00)00348-x · Cited by 61 · see citing works
Methanol synthesis from CO2 over Cu/ZnO catalysts prepared from various coprecipitated precursors
Fujita, S., Kanamori, Y., Satriyo, A. M., & Takezawa, N. (1998) · Catalysis Today · doi:10.1016/s0920-5861(98)00222-3 · Cited by 60 · see citing works
Low-pressure DME synthesis with Cu-based hybrid catalysts using temperature-gradient reactor
Omata, K., Watanabe, Y., Umegaki, T., Ishiguro, G., & Yamada, M. (2002) · Fuel · doi:10.1016/s0016-2361(02)00080-7 · Cited by 55 · see citing works
Self-citationWaugh, K. C. (1999) · Catalysis Letters · doi:10.1023/a:1019025816831 · Cited by 44 · see citing works
Activity and stability of Cu/ZnO/Al2O3 catalyst promoted with B2O3 for methanol synthesis
Wu, J., Saito, M., & Mabuse, H. (2000) · Catalysis Letters · doi:10.1023/a:1019010831562 · Cited by 37 · see citing works
Application of a New Color Detection Based Method for the Fast Parallel Screening of DeNOx Catalysts
Busch, O. M., Hoffmann, C., Johann, T. R. F., et al. (2002) · Journal of the American Chemical Society · doi:10.1021/ja020629x · Cited by 36 · see citing works
Umegaki, T., Watanabe, Y., Nukui, N., Omata, K., & Yamada, M. (2003) · Energy & Fuels · doi:10.1021/ef020241n · Cited by 34 · see citing works
Self-citationOmata, K., Watanabe, Y., Umegaki, T., et al. (2003) · Journal of the Japan Petroleum Institute · doi:10.1627/jpi.46.328 · Cited by 17 · see citing works
Self-citationOmata, K., Ishiguro, G., & Yamada, M. (2000) · 石油学会誌 · doi:10.1627/jpi1958.43.317 · Cited by 15 · see citing works
Self-citationOptimization of Cu-Zn-Al Oxide Catalyst for Methanol Synthesis Using Genetic Algorithm.
Omata, K., Umegaki, T., Ishiguro, G., et al. (2001) · 石油学会誌 · doi:10.1627/jpi1958.44.327 · Cited by 14 · see citing works
Self-citationUmegaki, T., Omata, K., Ishiguro, G., Watanabe, Y., & Yamada, M. (2003) · Journal of the Japan Petroleum Institute · doi:10.1627/jpi.46.181 · Cited by 13 · see citing works
Self-citationOmata, K., Umegaki, T., Watanabe, Y., et al. (2003) · Journal of the Japan Petroleum Institute · doi:10.1627/jpi.46.189 · Cited by 13 · see citing works
Self-citationOmata, K., Umegaki, T., Watanabe, Y., et al. (2002) · Journal of the Japan Petroleum Institute · doi:10.1627/jpi.45.192 · Cited by 13 · see citing works
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Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Discovery and Optimization of Materials Using Evolutionary Approaches
Le, T. C., & Winkler, D. A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00691 · Cited by 204 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
- Authors
- Watanabe, Y., Umegaki, T., Hashimoto, M., Omata, K., & Yamada, M.
- Affiliations
- Tohoku University (JP)
- Corresponding author
- Yamada, Muneyoshi
- ORCID
- Umegaki, Tetsuo
- Venue
- Catalysis Today
- Publisher
- Elsevier BV
- Published
- 2004-06
- Volume
- 89
- Issue
- 4
- Pages
- 455-464
- ISSN
- 0920-5861 (ISSN-L)
- OpenAlex
- W2027439528
- Cited by
- 51
- References
- 35
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2017: 1 · 2016: 2 · 2012: 1
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Catalysis Today (publishedVersion)
- Funders
- Japan Society for the Promotion of Science
- Ministry of Education, Culture, Sports, Science and Technology
- Topics
- Physical Sciences › Chemical Engineering › Catalysis › Catalysts for Methane Reforming
- Physical Sciences › Chemical Engineering › Process Chemistry and Technology › Carbon dioxide utilization in catalysis
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- methanol synthesis, syngas, high-throughput screening, 96-well plate, artificial neural networks, genetic algorithm, catalyst composition optimization, calcination temperature
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