Calculations of thermodynamical properties of U3Si2

International audienceUranium silicide compounds, especially U3Si2, have been studied as a potential replacement for uranium dioxide fuel in pressurized water reactors. These compounds offer several superior properties as a larger thermal conductivity and a higher uranium density. The thermodynamical quantities (specific heat, thermal expansion and conductivity…) are crucial to drive the fuel design. They are directly related to the atomic vibrations and more specifically, they can be extracted from the phonon spectra and the crystal structure.In this work we calculate the phonon spectra of different structures of the U3Si2 compound, up to the meltin curve, by taking into account the anharmonic effects and by using the TDEP method[1]. The thermodynamical properties extracted from these phonon spectra will then be compared to the previous calculations and the available experimental data. We show that the unique features of the crystallographic structure of this compound drive its behaviour in temperature

Calculations of thermodynamical properties of U3Si2

International audienceUranium silicide compounds, especially U3Si2, have been studied as a potential replacement for uranium dioxide fuel in pressurized water reactors. These compounds offer several superior properties as a larger thermal conductivity and a higher uranium density. The thermodynamical quantities (specific heat, thermal expansion and conductivity…) are crucial to drive the fuel design. They are directly related to the atomic vibrations and more specifically, they can be extracted from the phonon spectra and the crystal structure.In this work we calculate the phonon spectra of different structures of the U3Si2 compound, up to the meltin curve, by taking into account the anharmonic effects and by using the TDEP method[1]. The thermodynamical properties extracted from these phonon spectra will then be compared to the previous calculations and the available experimental data. We show that the unique features of the crystallographic structure of this compound drive its behaviour in temperature

Ab initio Canonical Sampling based on Variational Inference

International audienceFinite temperature calculations, based on ab initio molecular dynamics (AIMD) simulations, are a powerful tool able to predict material properties that cannot be deduced from ground state calculations. However, the high computational cost of AIMD limits its applicability for large or complex systems. To circumvent this limitation we introduce a new method named Machine Learning Assisted Canonical Sampling (MLACS), which accelerates the sampling of the Born--Oppenheimer potential surface in the canonical ensemble. Based on a self-consistent variational procedure, the method iteratively trains a Machine Learning Interatomic Potential to generate configurations that approximate the canonical distribution of positions associated with the ab initio potential energy. By proving the reliability of the method on anharmonic systems, we show that the method is able to reproduce the results of AIMD with an ab initio accuracy at a fraction of its computational cost