Catalyst Design: Knowledge Extraction From High-Throughput Experimentation

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