B1 to B2 structural phase transition in LiF under pressure

Jain, Aayushi; Dixit, R. C.

doi:10.1063/1.5032633

AIP Conference Proceedings

2018

DOI: 10.1063/1.5032633

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B1 to B2 structural phase transition in LiF under pressure

Aayushi Jain¹,

R. C. Dixit²

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Cited by 2 publications

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“…Figure 1. DFT versus NN predictions for (a) pair interactions for Li−F, Li−Li, and F−F and (b) equation of state for the bulk B1 phase LiF at 0 K 30. …”

mentioning

confidence: 99%

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

Lam

Ballinger

et al. 2021

ACS Appl. Mater. Interfaces

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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–33.3%BeF2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation 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 (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict 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 time scales (e.g., nanosecond) and large system sizes (e.g., 105 atoms) while maintaining ab initio density functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties.

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“…Figure 1. DFT versus NN predictions for (a) pair interactions for Li−F, Li−Li, and F−F and (b) equation of state for the bulk B1 phase LiF at 0 K 30. …”

mentioning

confidence: 99%

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

Lam

Ballinger

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

Raman spectra investigation on the pressure-induced phase transition in titanium nitride (TiN)

Cheng

Zeng

et al. 2020

AIP Advances

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Titanium nitride (TiN) exhibits extremely high wear resistance and corrosion resistance, leading to a broad range of applications. Despite the extensive theoretical efforts on the structural stability of TiN under pressure, a few experimental observations of the pressure-induced phase transition through x-ray diffraction are still in debate. Here, the Raman spectra of TiN under pressure are studied in a diamond anvil cell up to 36 GPa. The discontinuity in the pressure dependent phonon frequencies suggests a phase transition at around 5 GPa with the bonding between Ti and N hardly changing. The phase transition is ascribed to an isostructural transition or a vacancy filling mechanism.

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B1 to B2 structural phase transition in LiF under pressure

Cited by 2 publications

References 11 publications

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

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

Raman spectra investigation on the pressure-induced phase transition in titanium nitride (TiN)

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