scholarly journals Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential

2021 ◽  
Author(s):  
Stephen T. Lam ◽  
Qing-Jie Li ◽  
Ronald Ballinger ◽  
Charles Forsberg ◽  
Ju Li
Keyword(s):  
Neural Network ◽  
Molten Salts ◽  
Interatomic Potential

A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3

2020 ◽  
Vol 117 (15) ◽  
pp. 152102
Author(s):  
Ruiyang Li ◽  
Zeyu Liu ◽  
Andrew Rohskopf ◽  
Kiarash Gordiz ◽  
Asegun Henry ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Deep Neural Network ◽  
Interatomic Potential

Application of Artificial Neural Network for Prediction of Boride Layer Depth Obtained on XC38 Steel in Molten Salts

2015 ◽  
Vol 365 ◽  
pp. 194-199 ◽  
Author(s):  
Karim Rayane ◽  
Omar Allaoui
Keyword(s):  
Neural Network ◽  
Molten Salts ◽  
Good Accuracy ◽  
Back Propagation ◽  
Boride Layer ◽  
Network System ◽  
Back Propagation Algorithm ◽  
Layer Depth ◽  
Silicon Carbides ◽  
Neural Network System

This paper discusses an application of neural network system on the performance of boride layer thickness. Boriding treatment was carried out in three different molten salts consisting of borax (Na2B4O7) added to boron carbide (B4C), aluminum (Al) and silicon carbides (SiC). The substrate used in this study was XC38 steel. Borides layers involved in this work was obtained from a boriding treatment at the temperature range of 800-1050 °C with 50°C interval for 2, 4 and 6 h. A numerical experiment using normalized and binarized values was carried out, using a back-propagation algorithm in ANN. The modeling shows that for the three bath the depth of boride layer was predicted with good accuracy, with a highest performance of normalized values along experimental data range.

Development of an interatomic potential for Fe-He by neural network

2021 ◽  
Vol 196 ◽  
pp. 110549
Author(s):  
Hang Min ◽  
Feifeng Wu ◽  
Jiaqiang Yang ◽  
Xianbao Duan ◽  
Yanwei Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Interatomic Potential

scholarly journals Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials

2021 ◽  
Author(s):  
Stephen T. Lam ◽  
Qing-Jie Li ◽  
Ronald Ballinger ◽  
Charles Forsberg ◽  
Ju Li
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Molten Salts ◽  
Solid Phase ◽  
Accurate Method ◽  
Operating Conditions ◽  
Crystalline Solid ◽  
Interatomic Potentials ◽  
Nuclear Systems ◽  
Thermodynamic Conditions

<p>Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3¾F<sub>2</sub> (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts the structures and dynamics at normal operating conditions, high temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long timescales (e.g., nanosecond) and large system sizes (e.g., 10<sup>5</sup> atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties.</p>

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