What is it about?
A common aim is to predict the crystal structure of ionic solids at varying pressure, temperature or chemical composition (p-T-X), often from first principles. A method is introduced here of exploiting the experimental structural data obtained for a given mineral (in this case, olivine) at particular (p-T-X)-values for this purpose. These data are transformed into two ionic networks, one anionic and the other cationic [Ionic Network Analysis, or INA]. Since the network parameters (lengths and angles) vary systematically with p-T-X, their values at p-T-X between experimental points may be predicted. Reverse transformation from the network to the standard crystallographic representation yields the crystal structural data.
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Why is it important?
Owing to vastly improved experimental techniques in recent years, there are many sets of "good data" available, these relating to minerals at varying temperature and pressure. There is a need to bundle these data appropriately for predictions of crystal structure. Crystal structure prediction (CSP) is not necessarily an activity reserved for "theoreticians": experimental crystallographers can also get involved!
Perspectives
The ionic networks lead to a deeper understanding of the structures of minerals and their response to varying temperature and pressure. On the one hand, the constraints of chemical bonding become apparent. On the other, the more adjustable non-bonded structural features come to light.
Professor Noel William Thomas
Hochschule Koblenz
Read the Original
This page is a summary of: Crystal structure prediction by ionic network analysis: the example of (p–T–X)-structure relationships in olivines, Acta Crystallographica Section B Structural Science Crystal Engineering and Materials, January 2017, International Union of Crystallography,
DOI: 10.1107/s2052520616018308.
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