The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
Combinatorial and High‐Throughput Materials Science
Abstract
There is increasing acceptance of high‐throughput technologies for the discovery, development, and optimization of materials and catalysts in industry. Over the years, the relative synchronous development of technologies for parallel synthesis and characterization has been accompanied by developments in associated software and information technologies. This Review aims to provide a comprehensive overview on the state of the art of the field by selected examples. Technologies developed to aid research on complex materials are covered as well as databases, design of experiment, data‐mining technologies, modeling approaches, and evolutionary strategies for development. Different methods for parallel synthesis provide single sample libraries, gradient libraries for electronic or optical materials, similar to polymers and catalysts, and products produced through formulation strategies. Many examples illustrate the variety of isolated solutions and document the barely recognized variety of new methods for the synthesis and analysis of almost any material. The Review ends with a summary of success stories and statements on still‐present problems and future tasks.
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Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Oliver, W. C., & Pharr, G. M. (1992) · Journal of Materials Research · doi:10.1557/jmr.1992.1564 · Cited by 26,961
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
New Class of Materials: Half-Metallic Ferromagnets
de Groot, R. A., Mueller, F. M., Engen, P. G. V., & Buschow, K. H. J. (1983) · Physical Review Letters · doi:10.1103/physrevlett.50.2024 · Cited by 4,730
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
Data Mining: Concepts and Techniques
Thamaraiselvi, G., & Kaliammal, A. (2014) · Journal of Information and Knowledge · Cited by 4,088
Neural networks — A comprehensive foundation
Wilusz, T. (1995) · Neurocomputing · doi:10.1016/0925-2312(95)90026-8 · Cited by 2,868
Haynes, C. L., & Van Duyne, R. P. (2001) · The Journal of Physical Chemistry B · doi:10.1021/jp010657m · Cited by 2,500
Applied Multivariate Statistical Analysis
Härdle, W., & Simar, L. (2003) · Springer eBooks · doi:10.1007/978-3-662-05802-2 · Cited by 2,263
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The high-throughput highway to computational materials design
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Next-Generation Experimentation with Self-Driving Laboratories
Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019) · Trends in Chemistry · doi:10.1016/j.trechm.2019.02.007 · Cited by 426
Kreutz, J. E., Shukhaev, A., Du, W., Druskin, S., Daugulis, O., & Ismagilov, R. F. (2010) · Journal of the American Chemical Society · doi:10.1021/ja909853x · Cited by 126
Soliman, A., AlAmoodi, N., Karanikolos, G. N., Doumanidis, C. C., & Polychronopoulou, K. (2020) · Nanomaterials · doi:10.3390/nano10112198 · Cited by 53
Knowledge-guided large language model for material science
Wang, G., Hu, J., Zhou, J., Liu, S., Li, Q., & Sun, Z. (2025) · Review of Materials Research · doi:10.1016/j.revmat.2025.100007 · Cited by 16
Metadata
- Authors
- Maier, W. F., Stöwe, K., & Sieg, S.
- Venue
- Angewandte Chemie International Edition
- Publisher
- Wiley
- Published
- 2007-08-03
- Volume
- 46
- Issue
- 32
- Pages
- 6016-6067
- ISSN
- 1433-7851 (ISSN-L)
- 1521-3773
- OpenAlex
- W2140767804
- Cited by
- 401
- References
- 143
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- closed
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