تقرير
A machine learning route between band mapping and band structure
| العنوان: | A machine learning route between band mapping and band structure |
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| المؤلفون: | Xian, Rui Patrick, Stimper, Vincent, Zacharias, Marios, Dendzik, Maciej, Dong, Shuo, Beaulieu, Samuel, Schölkopf, Bernhard, Wolf, Martin, Rettig, Laurenz, Carbogno, Christian, Bauer, Stefan, Ernstorfer, Ralph |
| سنة النشر: | 2020 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Physics - Data Analysis, Statistics and Probability, Condensed Matter - Materials Science, Physics - Computational Physics |
| الوصف: | Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials. While convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting quasiparticle dispersion (closely related to BS) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1038/s43588-022-00382-2 |
| URL الوصول: | http://arxiv.org/abs/2005.10210 |
| رقم الأكسشن: | edsarx.2005.10210 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1038/s43588-022-00382-2 |
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