Title: Uncertainty quantification of DFT-predicted finite temperature thermodynamic properties within the Debye model

Abstract

Density functional theory (DFT) calculations are routinely used to screen for functional materials for a variety of applications. This screening is often carried out with a few descriptors, which use ground-state properties that typically ignore finite temperature effects. Finite-temperature effects can be included by calculating the vibration properties, and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional-van der Waals (BEEF-vdW). The vibrational properties are estimated using the Debye model, and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al₂O₃, Al, Ca, and GaAs. In the case of Li, Li₂O, and NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by the BEEF-vdW ensemble is typicallymore » normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution. Given the computational efficiency of the approach, we believe that uncertainty quantification should be routinely incorporated into finite-temperature predictions. In order to facilitate this, we have open-sourced the code base under the name dePye.« less

Authors:

Guan, Pin-Wen  ^[1];

• Search DOE PAGES for author "Guan, Pin-Wen"
• Search DOE PAGES for ORCID "0000-0002-4484-3216"

Houchins, Gregory  ^[1];

• Search DOE PAGES for author "Houchins, Gregory"
• Search DOE PAGES for ORCID "0000-0002-6858-9267"

Viswanathan, Venkatasubramanian  ^[1]

• Search DOE PAGES for author "Viswanathan, Venkatasubramanian"
• Search DOE PAGES for ORCID "0000-0003-1060-5495"

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+ Show Author Affiliations

1. Carnegie Mellon Univ., Pittsburgh, PA (United States)

Publication Date:

Mon Dec 23 00:00:00 EST 2019

Research Org.:

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Org.:

USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:

1799384

Alternate Identifier(s):

OSTI ID: 1580166

Grant/Contract Number:  

EE0007810

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Chemical Physics

Additional Journal Information:

Journal Volume: 151; Journal Issue: 24; Journal ID: ISSN 0021-9606

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; Physics; Exchange correlation functionals; Debye model; Thermodynamic functions; Thermodynamic properties; Vibrational properties; Bulk modulus; Entropy; Density functional theory