The role of decomposition reactions in assessing first-principles predictions of solid stability

Abstract

The performance of density functional theory (DFT) approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determined enthalpies of formation from elemental phases, ΔHf. However, a compound competes thermodynamically with both other compounds and their constituent elemental forms, and thus, the enthalpies of the decomposition reactions to these competing phases, ΔHd, determines thermodynamic stability. We evaluated the phase diagrams for 56,791 compounds to classify decomposition reactions into three types: 1. those that produce elemental phases, 2. those that produce compounds, and 3. those that produce both. This analysis shows that the decomposition into elemental forms is rarely the competing reaction that determines compound stability and that approximately two-thirds of decomposition reactions involve no elemental phases. Using experimentally reported formation enthalpies for 1,012 solid compounds, we assess the accuracy of the generalized gradient approximation (GGA) (PBE) and meta-GGA (SCAN) density functionals for predicting compound stability. For 646 decomposition reactions that are not trivially the formation reaction, PBE (MAD = 70 meV/atom) and SCAN (MAD = 59 meV/atom) perform similarly, and commonly employed correction schemes using fitted elemental reference energies make only a negligible improvement (~2 meV/atom). Furthermore, for 231 reactions involving only compounds (Type 2), the agreement between SCAN, PBE, and experiment is within ~35 meV/atom and is thus comparable to the magnitude of experimental uncertainty.

References in corpus (10)

• Design of Lead-Free Inorganic Halide Perovskites for Solar Cells via Cation-Transmutation
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• A Map of the Inorganic Ternary Metal Nitrides
• Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry
• Deep Neural Networks for Accurate Predictions of Garnet Stability
• The Geometric Blueprint of Perovskites
• Material Discovery and Design Principles for Stable, High Activity Perovskite Cathodes for Solid Oxide Fuel Cells
• Performance of the strongly constrained and appropriately normed density functional for solid-state materials
• Evaluating transition-metal oxides within DFT-SCAN and SCAN+U frameworks for solar thermochemical applications
• Assessing the SCAN functional for itinerant electron ferromagnets

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