Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds
| Autor: | Elsa Olivetti, Shengtong Zhang, Peiwen Ren, Daniel W. Apley, Kyle D. Miller, Alexandru B. Georgescu, Aubrey R. Toland, James M. Rondinelli, Nicholas Wagner |
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| Rok vydání: | 2021 |
| Předmět: |
Materials science
General Chemical Engineering Energy transfer Feature vector FOS: Physical sciences Binary number Insulator (electricity) 02 engineering and technology computer.software_genre 01 natural sciences Condensed Matter - Strongly Correlated Electrons Covalent radius 0103 physical sciences Materials Chemistry Microelectronics Metal–insulator transition 010306 general physics Condensed Matter - Materials Science Strongly Correlated Electrons (cond-mat.str-el) Database business.industry Probabilistic logic Materials Science (cond-mat.mtrl-sci) General Chemistry 021001 nanoscience & nanotechnology 0210 nano-technology business computer |
| Zdroj: | Chemistry of Materials. 33:5591-5605 |
| ISSN: | 1520-5002 0897-4756 |
| DOI: | 10.1021/acs.chemmater.1c00905 |
| Popis: | Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file. |
| Databáze: | OpenAIRE |
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