Uhrin, M;
(2015)
Understanding the Structure of Materials at the Intersection of Rationalisation, Prediction and Big Data.
Doctoral thesis , UCL (University College London).
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Abstract
Theoretical materials science has a large and growing role to play in modern society thanks to its ability to deliver materials with new and interesting properties. The properties of any material are, on some level, a function of its internal structure. In this work we combine three important tools spanning the last 100 years of materials research, rationalisation, prediction and big data in an attempt to understand the factors that underpin the stability of ordered structures and to build an understanding of structure that is agnostic of a particular element or building block. We apply rationalisation to data mining of the Inorganic Crystal Structure Database, using various proposed structure descriptors to probe the factors affecting structure stability. Extensive prediction is performed on the Fe-Ni-Si system at inner earth core pressures to determine the phases most likely to be present, yielding a new, stable, Ni-Si structure. A new prediction technique for 2D grain boundaries is presented that doubles the size of system that can reasonably be studied at the ab initio level of theory. The structurally rich phosphorus and arsenic systems are investigated using structure prediction, producing new metastable structures. Finally, we use a simple model for particles that attract at long range and repel at short to probe all the possible binary structures over a wide range of stoichiometries. By carrying out prediction over a wide range of potential parameters we build a database of almost 20M entries. Contained within are a number of unreported structures including many in parts of parameter space that go beyond the periodic table in terms of size and bond energy ratios. Our work provides hints that these hypothetical structures could be realised in self assembling systems made up from constituents with tunable interactions opening the door to the possibility of new properties.
| Type: | Thesis (Doctoral) |
|---|---|
| Title: | Understanding the Structure of Materials at the Intersection of Rationalisation, Prediction and Big Data |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Keywords: | physics, materials, structure prediction, big data, data mining, condensed matter, structure rationalisation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1470276 |
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