scholarly journals Classification of local chemical environments from x-ray absorption spectra using supervised machine learning

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Matthew R. Carbone ◽  
Shinjae Yoo ◽  
Mehmet Topsakal ◽  
Deyu Lu
Keyword(s):  
Machine Learning ◽  
Absorption Spectra ◽  
Supervised Machine Learning ◽  
X Ray ◽  
X Ray Absorption

scholarly journals A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

2020 ◽  
Author(s):  
Conor Rankine ◽  
Marwah Madkhali ◽  
Thomas Penfold
Keyword(s):  
Neural Network ◽  
Absorption Spectra ◽  
Deep Neural Network ◽  
Theoretical Calculations ◽  
End Users ◽  
Supervised Machine Learning ◽  
Strong Impact ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

<p>X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. <i>in operando</i> catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less </p><p>than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.</p>

scholarly journals Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

2020 ◽  
Vol 124 (15) ◽  
Author(s):  
Matthew R. Carbone ◽  
Mehmet Topsakal ◽  
Deyu Lu ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Absorption Spectra ◽  
X Ray ◽  
Quantitative Accuracy ◽  
X Ray Absorption

scholarly journals A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

2020 ◽  
Author(s):  
Conor Rankine ◽  
Marwah Madkhali ◽  
Thomas Penfold
Keyword(s):  
Neural Network ◽  
Absorption Spectra ◽  
Deep Neural Network ◽  
Theoretical Calculations ◽  
End Users ◽  
Supervised Machine Learning ◽  
Strong Impact ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

<p>X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. <i>in operando</i> catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less </p><p>than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.</p>

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy

2021 ◽  
Author(s):  
Samantha Tetef ◽  
Niranjan Govind ◽  
Gerald T. Seidler
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Emission Spectroscopy ◽  
Emission Spectra ◽  
Relevant Information ◽  
Unsupervised Machine Learning ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

We utilize unsupervised machine learning to extract chemically relevant information in X-ray absorption near-edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of an ensemble of sulphorganic molecules.

Automatic oxidation threshold recognition of XAFS data using supervised machine learning

2019 ◽  
Vol 4 (5) ◽  
pp. 1014-1018 ◽  
Author(s):  
Itsuki Miyazato ◽  
Lauren Takahashi ◽  
Keisuke Takahashi
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Supervised Machine Learning ◽  
Oxidation States ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

Oxidation states of materials are characterized by the X-ray absorption near edge structure (XANES) region in X-ray absorption spectroscopy (XAS).

Electronic Structure and X-ray Absorption Spectra of Rutile TiO2 Using First-Principles Calculations

2014 ◽  
Vol 52 (12) ◽  
pp. 1025-1029
Author(s):  
Min-Wook Oh ◽  
Tae-Gu Kang ◽  
Byungki Ryu ◽  
Ji Eun Lee ◽  
Sung-Jae Joo ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Absorption Spectra ◽  
First Principles ◽  
First Principles Calculations ◽  
Rutile Tio2 ◽  
X Ray ◽  
X Ray Absorption
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