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The importance of the Lieb-Simon proof of the relative exactness of Thomas-Fermi theory in the large-Z limit to modern density functional theory (DFT) is explored. The principle, that there is a specific semiclassical limit in which functionals become local, implies that there exist well-defined leading functional corrections to local approximations that become relatively exact for the error in local approximations in this limit. It is argued that this principle might be used to greatly improve the accuracy of the thousand or so DFT calculations that are now published each week. A key question is how to find the leading corrections to any local density approximation as this limit is approached. These corrections have been explicitly derived in ridiculously simple model systems to ridiculously high order, yielding ridiculously accurate energies. Much analytic work is needed to use this principle to improve realistic calculations of molecules and solids.
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Leveraging Thermochemistry Data to Build Accurate Microkinetic Models
Ab initio microkinetic modeling, parameterized using density functional theory (DFT) energies, is a common tool to quantify reaction rates and analyze reaction mechanisms a priori in heterogeneous catalysis. Such models, however, often have large prediction errors even if they include plausible reaction steps and correctly model the active sites; this is partially due to the intrinsic inaccuracies of the chosen DFT functional. Borrowing concepts from Bayesian calibration theory, we show that transferable data-driven corrections to DFT energies in the form of Gaussian process models trained on single-crystal adsorption calorimetry data can improve the accuracy of microkinetic models substantially. Specifically, we demonstrate that such corrections improve the predictive accuracy of the microkinetic model of the water-gas shift reaction on single-crystal Cu(111) surface by 3 orders of magnitude. We finally show that Gaussian process corrections serve as informed priors in a Bayesian experimental design framework to learn an accurate a posteriori microkinetic model from few kinetic experiments. We posit that these results suggest that even infusing small, related, high-fidelity thermochemistry data, when available, can systematically and substantially improve the predictive accuracy of microkinetic models.
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- Award ID(s):
- 1804104
- PAR ID:
- 10189596
- Date Published:
- Journal Name:
- The journal of physical chemistry
- Volume:
- 124
- ISSN:
- 1520-5207
- Page Range / eLocation ID:
- 5740–5748
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.jpcc.0c00491
