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Mar 26, 2020

ACS Spring 2020 National Meeting & Expo

Harnessing data sets for more accurate kinetic parameter estimation: Bayesian parameter estimation to include errors from both experiment and theory

Bayesian Parameter Estimation

BPE

CheKiPEUQ

Uncertainty

Error Propagation

Uncertainty Propagation

Error

MCMC

Parameter Estimation

Theory

Abstract

11

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11

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Abstract

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Keywords

Bayesian Parameter Estimation

BPE

CheKiPEUQ

Uncertainty

Error Propagation

Uncertainty Propagation

Error

MCMC

Parameter Estimation

Theory

Abstract

The future predictions of catalysts that deviate from trends will require increased accuracy in simulations, which can be enhanced by considering uncertainty extracted from data sets. Data sets can provide estimates of uncertainty (such as experimental error from noise as well as the errors from density functional theory calculations of energies). Inclusion of these uncertainties can enhance calculation of the best estimates for chemical kinetic parameters. Bayesian Parameter Estimation (BPE), based on Bayes Theorem, enables finding the estimated values with maximum probability considering what is physically realistic (see ACS Catal.2019, 98, 6624-6647, https://pubs.acs.org/doi/abs/10.1021/acscatal.9b01234). In this presentation, one or two examples will be presented for temperature programmed reactions following adsorption of adsorbates on surfaces.<br/><br/>

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© Copyright 2019 Morressier GmbH.
All rights reserved.