Charting the complete elastic properties of inorganic crystalline compounds

By Maarten de Jong, Wei Chen, Thomas Angsten, Anubhav Jain, R. Notestine, A. Gamst, Marcel H. F. Sluiter, Chaitanya Krishna Ande, S. van der Zwaag, J. Plata and 5 more. 2015

958 citations recorded by the source corpus.

The elastic constant tensor of an inorganic compound provides a complete description of the response of the material to external stresses in the elastic limit. It thus provides fundamental insight into the nature of the bonding in the material, and it is known to correlate with many mechanical properties. Despite the importance of the elastic constant tensor, it has been measured for a very small fraction of all known inorganic compounds, a situation that limits the ability of materials scientists to develop new materials with targeted mechanical responses. To address this deficiency, we present here the largest database of calculated elastic properties for inorganic compounds to date. The database currently contains full elastic information for 1,181 inorganic compounds, and this number is growing steadily. The methods used to develop the database are described, as are results of tests that establish the accuracy of the data. In addition, we document the database format and describe the different ways it can be accessed and analyzed in efforts related to materials discovery and design. Design Type(s) observation design Measurement Type(s) elastic constant tensor Technology Type(s) stress-strain methodology Factor Type(s) Design Type(s) observation design Measurement Type(s) elastic constant tensor Technology Type(s) stress-strain methodology Factor Type(s) Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Geophysics, Materials Chemistry

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Citations in the corpus

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (2018, methodology): “52 The MEGNet models, similar to the SchNet models, utilize only one atomic attribute (atomic number) and one bond attribute (spatial distance), while nine attributes were used in the CGCNN model.”
An Unlikely Route to Low Lattice Thermal Conductivity: Small Atoms in a Simple Layered Structure (2018, methodology): “ predicted speed of sound (kL data can be found in Ref. [41]). The mean speed of sound, vs, was estimated using the calculated elastic moduli from MaterialsProject.org and experimental densities [8], [9]. The dashed line shows a kL µv3s dependence as a guide to the eye. Compounds with M = Mg are sho”
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