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The thermodynamic scale of inorganic crystalline metastability.

Science advances | 2016

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.

Pubmed ID: 28138514 RIS Download

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NumPy (tool)

RRID:SCR_008633

NumPy is the fundamental package needed for scientific computing with Python. It contains among other things: * a powerful N-dimensional array object * sophisticated (broadcasting) functions * tools for integrating C/C and Fortran code * useful linear algebra, Fourier transform, and random number capabilities. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Sponsored by ENTHOUGHT

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SciPy (tool)

RRID:SCR_008058

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Pymatgen (tool)

RRID:SCR_016565

Python library for materials analysis codes. Defines core object representations for structures and molecules.

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