AJ Medford

CW Gao

E Walker

HJ Monkhorst

J Wellendorff

JE Sutton

JK Nørskov

JS Hummelshøj

M Salciccioli

N Yang

P Erdrich

P Giannozzi

RN Gutenkunst

S Rangarajan

SRO Rangarajan

V Vorotnikov

AbstractSurface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.</jats:p

Zachary W. Ulissi

Andrew J. Medford

Thomas Bligaard

Jens K. Nørskov

Crossref

Nature Communications

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

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