Decoding defect statistics from diffractograms via machine learning
| Autor: | Surya R. Kalidindi, Rémi Dingreville, Cody Kunka, Elton Y. Chen, Apaar Shanker |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Diffraction
Computer science Population High resolution 02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Set (abstract data type) QA76.75-76.765 Statistics General Materials Science Computer software education Materials of engineering and construction. Mechanics of materials Statistic education.field_of_study business.industry 021001 nanoscience & nanotechnology 0104 chemical sciences Computer Science Applications Mechanics of Materials Modeling and Simulation TA401-492 State (computer science) Artificial intelligence 0210 nano-technology business computer Decoding methods |
| Zdroj: | npj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021) |
| ISSN: | 2057-3960 |
| Popis: | Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms. |
| Databáze: | OpenAIRE |
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