Decoding defect statistics from diffractograms via machine learning
Abstract 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...
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Nature Portfolio
2021
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oai:doaj.org-article:3cd3950f2ddd406b92a130b336fb67012021-12-02T14:58:31ZDecoding defect statistics from diffractograms via machine learning10.1038/s41524-021-00539-z2057-3960https://doaj.org/article/3cd3950f2ddd406b92a130b336fb67012021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00539-zhttps://doaj.org/toc/2057-3960Abstract 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.Cody KunkaApaar ShankerElton Y. ChenSurya R. KalidindiRémi DingrevilleNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021) |
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DOAJ |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Cody Kunka Apaar Shanker Elton Y. Chen Surya R. Kalidindi Rémi Dingreville Decoding defect statistics from diffractograms via machine learning |
| description |
Abstract 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. |
| format |
article |
| author |
Cody Kunka Apaar Shanker Elton Y. Chen Surya R. Kalidindi Rémi Dingreville |
| author_facet |
Cody Kunka Apaar Shanker Elton Y. Chen Surya R. Kalidindi Rémi Dingreville |
| author_sort |
Cody Kunka |
| title |
Decoding defect statistics from diffractograms via machine learning |
| title_short |
Decoding defect statistics from diffractograms via machine learning |
| title_full |
Decoding defect statistics from diffractograms via machine learning |
| title_fullStr |
Decoding defect statistics from diffractograms via machine learning |
| title_full_unstemmed |
Decoding defect statistics from diffractograms via machine learning |
| title_sort |
decoding defect statistics from diffractograms via machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/3cd3950f2ddd406b92a130b336fb6701 |
| work_keys_str_mv |
AT codykunka decodingdefectstatisticsfromdiffractogramsviamachinelearning AT apaarshanker decodingdefectstatisticsfromdiffractogramsviamachinelearning AT eltonychen decodingdefectstatisticsfromdiffractogramsviamachinelearning AT suryarkalidindi decodingdefectstatisticsfromdiffractogramsviamachinelearning AT remidingreville decodingdefectstatisticsfromdiffractogramsviamachinelearning |
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