Li(Ni,Co,Al)O2 Cathode Delithiation: A Combination of Topological Analysis, Density Functional Theory, Neutron Diffraction, and Machine Learning Techniques

2017 ◽  
Vol 121 (51) ◽  
pp. 28293-28305 ◽  
Author(s):  
Roman A. Eremin ◽  
Pavel N. Zolotarev ◽  
Olga Yu. Ivanshina ◽  
Ivan A. Bobrikov
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Neutron Diffraction ◽  
Density Functional ◽  
Topological Analysis ◽  
Machine Learning Techniques ◽  
Functional Theory ◽  
Learning Techniques

scholarly journals Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces

2021 ◽  
Author(s):  
Ismaeel Ramzan ◽  
Linghan Kong ◽  
Richard Bryce ◽  
Neil Burton
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Machine Learning Techniques ◽  
Functional Theory ◽  
Translational Invariance ◽  
Small Organic Molecules ◽  
Learning Techniques ◽  
Efficient Calculation ◽  
Force Components ◽  
The Cost

<p>We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and targets based on pairwise interatomic forces, to determine the Cartesian atomic forces suitable for use in molecular mechanics and dynamics calculations. The scheme implicitly maintains rotational and translational invariance and predicts Cartesian forces as a linear combination of a set of force components in an <a>interatomic basis.</a> We show that the method can predict the reconstructed Cartesian atomic forces for a set of small organic molecules to less than 2 kcal mol<sup>-1</sup> Å<sup>-1</sup> from the reference force values obtained via density functional theory. The pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic forces at a quantum mechanical level but at a fraction of the cost, providing a step towards the efficient calculation of accurate thermodynamic properties. </p>

scholarly journals Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces

2021 ◽  
Author(s):  
Ismaeel Ramzan ◽  
Linghan Kong ◽  
Richard Bryce ◽  
Neil Burton
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Machine Learning Techniques ◽  
Functional Theory ◽  
Translational Invariance ◽  
Small Organic Molecules ◽  
Learning Techniques ◽  
Efficient Calculation ◽  
Force Components ◽  
The Cost

<p>We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and targets based on pairwise interatomic forces, to determine the Cartesian atomic forces suitable for use in molecular mechanics and dynamics calculations. The scheme implicitly maintains rotational and translational invariance and predicts Cartesian forces as a linear combination of a set of force components in an <a>interatomic basis.</a> We show that the method can predict the reconstructed Cartesian atomic forces for a set of small organic molecules to less than 2 kcal mol<sup>-1</sup> Å<sup>-1</sup> from the reference force values obtained via density functional theory. The pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic forces at a quantum mechanical level but at a fraction of the cost, providing a step towards the efficient calculation of accurate thermodynamic properties. </p>

scholarly journals Polymorphs of Rb3ScF6: X-ray and Neutron Diffraction, Solid-State NMR, and Density Functional Theory Calculations Study

2021 ◽  
Vol 60 (8) ◽  
pp. 6016-6026
Author(s):  
Aydar Rakhmatullin ◽  
Maxim S. Molokeev ◽  
Graham King ◽  
Ilya B. Polovov ◽  
Konstantin V. Maksimtsev ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Solid State ◽  
Neutron Diffraction ◽  
Solid State Nmr ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Functional Theory ◽  
X Ray

scholarly journals Unveiling the electrochemical mechanisms of Li2Fe(SO4)2 polymorphs by neutron diffraction and density functional theory calculations

2016 ◽  
Vol 18 (21) ◽  
pp. 14509-14519 ◽  
Author(s):  
Laura Lander ◽  
Marine Reynaud ◽  
Javier Carrasco ◽  
Nebil A. Katcho ◽  
Christophe Bellin ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Neutron Diffraction ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Unit Cell ◽  
Functional Theory ◽  
Electrochemically Active ◽  
Difference Fourier

The structure of electrochemically active Li1.5Fe(SO4)2 with difference Fourier maps highlighting the two lithium positions, which are also represented in the unit cell.

Predicting the stability of ternary intermetallics with density functional theory and machine learning

2018 ◽  
Vol 148 (24) ◽  
pp. 241728 ◽  
Author(s):  
Jonathan Schmidt ◽  
Liming Chen ◽  
Silvana Botti ◽  
Miguel A. L. Marques
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Density Functional ◽  
Functional Theory ◽  
The Stability
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