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Ab initio instanton rate theory made efficient using Gaussian process regression

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Abstract

Ab initio instanton rate theory is a computational method for rigorously including tunnelling effects into the calculations of chemical reaction rates based on a potential-energy surface computed on the fly from electronic-structure theory. This approach is necessary to extend conventional transition-state theory into the deep-tunnelling regime, but it is also more computationally expensive as it requires many more ab initio calculations. We propose an approach which uses Gaussian process regression to fit the potential-energy surface locally around the dominant tunnelling pathway. The method can be converged to give the same result as from an on-the-fly ab initio instanton calculation but it requires far fewer electronic-structure calculations. This makes it a practical approach for obtaining accurate rate constants based on high-level electronic-structure methods. We show fast convergence to reproduce benchmark H + CH4 results and evaluate new low-temperature rates of H + C2H6 in full dimensionality at a UCCSD(T)-F12b/cc-pVTZ-F12 level.

Graphical abstract: Ab initio instanton rate theory made efficient using Gaussian process regression

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Publication details

The article was received on 27 Apr 2018, accepted on 21 May 2018 and first published on 27 Jun 2018


Article type: Paper
DOI: 10.1039/C8FD00085A
Citation: Faraday Discuss., 2018, Advance Article
  • Open access: Creative Commons BY license
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    Ab initio instanton rate theory made efficient using Gaussian process regression

    G. Laude, D. Calderini, D. P. Tew and J. O. Richardson, Faraday Discuss., 2018, Advance Article , DOI: 10.1039/C8FD00085A

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