A machine learning approach to crystal structure prediction
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2007.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/42132 |
| _version_ | 1826192419031351296 |
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| author | Fischer, Christopher Carl |
| author2 | Gerbrand Ceder. |
| author_facet | Gerbrand Ceder. Fischer, Christopher Carl |
| author_sort | Fischer, Christopher Carl |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2007. |
| first_indexed | 2024-09-23T09:12:12Z |
| format | Thesis |
| id | mit-1721.1/42132 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:12:12Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/421322019-04-12T09:45:05Z A machine learning approach to crystal structure prediction Fischer, Christopher Carl Gerbrand Ceder. Massachusetts Institute of Technology. Dept. of Materials Science and Engineering. Massachusetts Institute of Technology. Dept. of Materials Science and Engineering. Materials Science and Engineering. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2007. Includes bibliographical references (p. 137-147). This thesis develops a machine learning framework for predicting crystal structure and applies it to binary metallic alloys. As computational materials science turns a promising eye towards design, routine encounters with chemistries and compositions lacking experimental information will demand a practical solution to structure prediction. We review the ingredients needed to solve this problem and focus on structure search. This thesis develops and argues for a search strategy utilizing a combination of machine learning and modern quantum mechanical methods. Structure correlations in a binary alloy database are extracted using probabilistic graphical models. Specific correlations are shown to reflect well-known structure stabilizing mechanisms. Two probabilistic models are investigated to represent correlation: an undirected graphical model known as a cumulant expansion, and a mixture model. The cumulant expansion is used to efficiently guide Density Functional Theory predictions of compounds in the Ag-Mg, Au-Zr, and Li-Pt alloy systems. Cross-validated predictions of compounds present in 1335 binary alloys are used to demonstrate predictive ability over a wide range of chemistries - providing both efficiency and confidence to the search problem. Inconsistencies present in the cumulant expansion are analyzed, and a formal correction is developed. Finally, a probabilistic mixture model is investigated as a means to represent correlation in a compact way. The mixture model leads to a significant reduction in model complexity while maintaining a level of prediction performance comparable to the cumulant expansion. Further analysis of the mixture model is performed in the context of classification. Unsupervised learning of alloy classes or groups is shown to reflect clear chemical trends. by Christopher Carl Fischer. Ph.D. 2008-09-03T14:40:55Z 2008-09-03T14:40:55Z 2007 2007 Thesis http://hdl.handle.net/1721.1/42132 228298537 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 147 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Materials Science and Engineering. Fischer, Christopher Carl A machine learning approach to crystal structure prediction |
| title | A machine learning approach to crystal structure prediction |
| title_full | A machine learning approach to crystal structure prediction |
| title_fullStr | A machine learning approach to crystal structure prediction |
| title_full_unstemmed | A machine learning approach to crystal structure prediction |
| title_short | A machine learning approach to crystal structure prediction |
| title_sort | machine learning approach to crystal structure prediction |
| topic | Materials Science and Engineering. |
| url | http://hdl.handle.net/1721.1/42132 |
| work_keys_str_mv | AT fischerchristophercarl amachinelearningapproachtocrystalstructureprediction AT fischerchristophercarl machinelearningapproachtocrystalstructureprediction |