dynamics simulations of molten magnesium chloride using machine-learning-based deep potential (Molecular Dynamics Inc)
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Molecular Dynamics Inc
dynamics simulations of molten magnesium chloride using machine-learning-based deep potential
Dynamics Simulations Of Molten Magnesium Chloride Using Machine Learning Based Deep Potential, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/dynamics+simulations+of+molten+magnesium+chloride+using+machine-learning-based+deep+potential/dynamics+simulations+of+molten+magnesium+chloride+using+machine+learning+based+deep+potential/pm36881968-260-18-10
Average 90 stars, based on 1 article reviews
Dynamics Simulations Of Molten Magnesium Chloride Using Machine Learning Based Deep Potential, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/dynamics+simulations+of+molten+magnesium+chloride+using+machine-learning-based+deep+potential/dynamics+simulations+of+molten+magnesium+chloride+using+machine+learning+based+deep+potential/pm36881968-260-18-10
Average 90 stars, based on 1 article reviews
dynamics simulations of molten magnesium chloride using machine-learning-based deep potential - by Bioz Stars,
2026-09
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other:Article Title: Development of Deep Potentials of Molten MgCl 2 -NaCl and MgCl 2 -KCl Salts Driven by Machine Learning. Article Snippet: Molten MgCl2-based chlorides have emerged as potential thermal storage and heat transfer materials due to high thermal stabilities and lower costs.. In this work, deep potential molecular dynamics (DPMD) simulations by a method combination of the first principle, classical molecular dynamics, and machine learning are performed to systemically study the relationships of structures and thermophysical properties of molten MgCl2− NaCl (MN) and MgCl2−KCl (MK) eutectic salts at the temperature range of 800−1000 K. The densities, radial distribution functions, coordination numbers, potential mean forces, specific heat capacities, viscosities, and thermal conductivities of these two chlorides are successfully reproduced under extended temperatures by DPMD with a larger size (5.2 nm) and longer timescale (5 ns).. It is concluded that the higher specific heat capacity of molten MK is originated from the strong potential mean force of Mg−Cl bonds, whereas the molten MN performs better in heat transfer due to the larger thermal conductivity and lower viscosity, attributed to the weak interaction between Mg and Cl ions. Article Title: Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl 2 -KCl Eutectic. Article Snippet: Theoretical studies on the MgCl2−KCl eutectic heavily rely on ab initio calculations based on density functional theory (DFT).. However, neither large-scale nor long-time calculations are feasible in the framework of the ab initio method, which makes it challenging to accurately predict some properties.. To address this issue, a scheme based on ab initio calculation, deep neural networks, and machine learning is introduced. |