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Machine Learning in Catalysis, the History of ML in Experimental Heterogeneous Catalysis: A Review
Abstract
Heterogeneous catalysis is used in nearly 80 per cent of all industrial chemical processes and contributes greatly to global GDP. As a consequence, catalytic discovery and optimization are of considerable importance to improve process performance and reduce costs for intermediate and commodity chemicals. The discovery and optimization process has traditionally been undertaken by the Edisonian trial-and-error strategy, which has been efficient but expensive and inefficient. More intelligent methods, such an experiment design (DOE), have improved the pace of catalytic exploration by offering an experimental structure. Analysis methods such as HTE have also led to an improvement in the discovery rate. Advances in computer hardware have led to breakthroughs in computational techniques such as density functional theory (DFT) that enable the development and evaluation of the in-silicon surface catalyst. Both of these methods have accelerated the exploration of catalysts by Edisonian approaches.
Keywords: Artificial intelligence technology, density functional theory (DFT), heterogeneous catalysis, machine learning in catalysis, materials genome initiative (MGI), metallic glasses (MG), oxidative dehydrogenation of ethane (ODHE)
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