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

2020 ◽  
Vol 124 (21) ◽  
pp. 4263-4270 ◽  
Author(s):  
C. D. Rankine ◽  
M. M. M. Madkhali ◽  
T. J. Penfold
Keyword(s):  
Neural Network ◽  
Absorption Spectra ◽  
Deep Neural Network ◽  
X Ray ◽  
Rapid Prediction ◽  
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 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 The Role of Structural Representation in the Performance of a Deep Neural Network for X-ray Spectroscopy

Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2715
Author(s):  
Marwah M.M. Madkhali ◽  
Conor D. Rankine ◽  
Thomas J. Penfold
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Chemical Space ◽  
Theoretical Calculations ◽  
Local Environment ◽  
Structural Representation ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space—the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC)—we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations.

X-ray absorption spectrum combined with deep neural network for on-line detection of beverage preservatives

2018 ◽  
Vol 89 (10) ◽  
pp. 103108
Author(s):  
Weifeng Hu ◽  
Siyuan Chen ◽  
Yuqi Li ◽  
Qian Wang ◽  
Zheng Fang
Keyword(s):  
Neural Network ◽  
Absorption Spectrum ◽  
Deep Neural Network ◽  
Line Detection ◽  
X Ray ◽  
On Line ◽  
X Ray Absorption

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

scholarly journals An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images

2021 ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Janmenjoy Nayak ◽  
Utkarsh Sinha ◽  
Vaibhav Arora ◽  
Uttam Ghosh ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
X Ray ◽  
Chest X Ray

scholarly journals Theoretical description of optical and x-ray absorption spectra of MgO including many-body effects

2021 ◽  
Vol 103 (19) ◽  
Author(s):  
Vijaya Begum ◽  
Markus E. Gruner ◽  
Christian Vorwerk ◽  
Claudia Draxl ◽  
Rossitza Pentcheva
Keyword(s):  
Absorption Spectra ◽  
Theoretical Description ◽  
Many Body ◽  
X Ray ◽  
Many Body Effects ◽  
X Ray Absorption

scholarly journals Soft X-ray absorption spectra in the CK region of carbon black and spectral analysis using the discrete variational Xα method

TANSO ◽  
2009 ◽  
Vol 2009 (236) ◽  
pp. 2-8 ◽  
Author(s):  
Yasuji Muramatsu ◽  
Ryusuke Harada ◽  
Muneyuki Motoyama ◽  
Eric M. Gullikson
Keyword(s):  
Spectral Analysis ◽  
Carbon Black ◽  
Absorption Spectra ◽  
X Ray ◽  
X Ray Absorption
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