The thermodynamic scale of inorganic crystalline metastability
By Wenhao Sun, S. Dacek, Shyue Ping Ong, Geoffroy Hautier, Anubhav Jain, W. Richards, A. Gamst, Kristin A. Persson, Gerbrand Ceder. Science Advances, 2016
823 citations recorded by the source corpus.
Data-mining the stability of 29,902 material phases reveals the thermodynamic landscape of inorganic crystalline metastability. The space of metastable materials offers promising new design opportunities for next-generation technological materials, such as complex oxides, semiconductors, pharmaceuticals, steels, and beyond. Although metastable phases are ubiquitous in both nature and technology, only a heuristic understanding of their underlying thermodynamics exists. We report a large-scale data-mining study of the Materials Project, a high-throughput database of density functional theory–calculated energetics of Inorganic Crystal Structure Database structures, to explicitly quantify the thermodynamic scale of metastability for 29,902 observed inorganic crystalline phases. We reveal the influence of chemistry and composition on the accessible thermodynamic range of crystalline metastability for polymorphic and phase-separating compounds, yielding new physical insights that can guide the design of novel metastable materials. We further assert that not all low-energy metastable compounds can necessarily be synthesized, and propose a principle of ‘remnant metastability’—that observable metastable crystalline phases are generally remnants of thermodynamic conditions where they were once the lowest free-energy phase.
Subfields: Atmospheric Science, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/3f93bceb7d1905f6c1091c11cde1520325d848f3
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Citations in the corpus
- Accelerating the discovery of materials for clean energy in the era of smart automation (2018, background): “In addition, new approaches to understanding metastability enable the energetic upper-bound of phase transitions between material polymorphs to be estimated 220 , and a growing body of classical force-field data 221 might be leveraged to approximate kinetics in a similar way.”
- Machine learning for a sustainable energy future (2022, background): “135,136 Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability.”
- Reactivity-Guided Interface Design in Na Metal Solid-State Batteries (2019, background): “14) Na3SbS4$8H2O structure was calculated to be only 11 meV/atom above the compositional convex hull, well within the range of energy where many synthesizable low-energy metastable compounds are found.(41) However, our efforts to isolate Na3SbS4$8H2O as a single phase were unsuccessful.”
- A perspective on conventional high-temperature superconductors at high pressure: Methods and materials (2019, background): “[344] were the first to address this question and suggested an order of energy differences of 100 meV/atom among polymorphs of the same stoichiometry.”
- Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries (2018, background): “, >100 meV/atom) may have a strong thermodynamic driving force for decomposition and may be difficult to synthesize.(57) Therefore,”
- Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, methodology): “1), [7, 27] suggesting that significantly greater accuracy would not be useful and the current model may be sufficiently accurate to use in place of full DFT calculations.”