Catalyst design: Knowledge extraction from high-throughput experimentation

J. M. Caruthers, J. A. Lauterbach, K. T. Thomson, V. Venkatasubramanian, C. M. Snively, A. Bhan, S. Katare, G. Oskarsdottir

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


We present a new framework for catalyst design that integrates computer-aided extraction of knowledge with high-throughput experimentation (HTE) and expert knowledge to realize the full benefit of HTE. We describe the current state of HTE and illustrate its speed and accuracy using an FTIR imaging system for oxidation of CO over metals. However, data is just information and not knowledge. In order to more effectively extract knowledge from HTE data, we propose a framework that, through advanced models and novel software architectures, strives to approximate the thought processes of the human expert. In the forward model the underlying chemistry is described as rules and the data or predictions as features. We discuss how our modeling framework - via a knowledge extraction (KE) engine - transparently maps rules-to-equations-to-parameters-to-features as part of the forward model. We show that our KE engine is capable of robust, automated model refinement, when modeled features do not match the experimental features. Further, when multiple models exist that can describe experimental data, new sets of HTE can be suggested. Thus, the KE engine improves (i) selection of chemistry rules and (ii) the completeness of the HTE data set as the model and data converge. We demonstrate the validity of the KE engine and model refinement capabilities using the production of aromatics from propane on H-ZSM-5. We also discuss how the framework applies to the inverse model, in order to meet the design challenge of predicting catalyst compositions for desired performance.

Original languageEnglish (US)
Pages (from-to)98-109
Number of pages12
JournalJournal of Catalysis
Issue number1-2
StatePublished - 2003
Externally publishedYes

Bibliographical note

Funding Information:
We acknowledge the encouragement and invaluable insight provided by our colleague Professor Nick Delgass. The work has been financially supported by the Indiana 21st Century Research and Technology Fund, ExxonMobil, NSF: CTS-0071730 (JAL), Purdue Research Foundation Fellowships (SK and GO), and the National Computational Science Alliance (NCSA) for supercomputing support: ESC030001.


  • Catalyst design
  • High throughput experimentation
  • Knowledge extraction
  • Zeolite


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