(661i) Catalysts on Amorphous Supports: Machine Learning Tools to Estimate Ab Initio site-Averaged Kinetics | AIChE

(661i) Catalysts on Amorphous Supports: Machine Learning Tools to Estimate Ab Initio site-Averaged Kinetics

Authors 

Vandervelden, C. - Presenter, University of California-Santa Barbara
Peters, B., University of Iliinois
Khan, S., University of California Santa Barbara
Scott, S., University of California, Santa Barbara
Electronic structure calculations have greatly advanced our understanding of homogeneous catalysts and crystalline heterogeneous catalysts. However, amorphous heterogeneous catalysts (e.g. Cr/SiO2 for olefin polymerization1 and WO3/SiO2 for olefin metathesis2) remain poorly understood. The principle difficulties are i) The nature of the disorder is quenched and unknown. (ii) Each active site has a different local environment and activity. (iii) Active sites are rare, often less than ~20% depending on the catalyst and preparation method. Few (if any) studies have ever attempted to compute site-averaged kinetics because the Arrhenius dependence on variable activation energies leads to an exponential average that requires an intractable number of electronic structure calculations to. We present a new algorithm using machine learning techniques (metric learning kernel regression) and importance sampling to efficiently learn the distribution of activation energies. We demonstrate the algorithm by computing the site-averaged activity of a model amorphous catalyst with quenched disorder.

References:

  1. McDaniel, M. P. Catal. 2010, 53, 123-606
  2. Mol, J. C. Mol. Catal. A: Chem. 2004, 213, 39