Lattice Anharmonicity in Solids: High-Throughput Screening and Machine Learning
DescriptionDespite important breakthroughs in the last decade, the calculation of temperature dependent properties of solids still remains a challenging task, especially in the vicinity of structural phase transitions [1]. In this talk, I will show examples of computational high-throughput screening of thermal [2] and dielectric [3] properties, focusing on the example of oxide and fluoride perovskites. I will also discuss how machine learning can be used to accelerate such a screening [2] or to go beyond the current capabilities of plain density functional theory [4].
[1] A. van Roekeghem, J. Carrete and N. Mingo, Quantum Self-Consistent Ab-Initio Lattice Dynamics, Comp. Phys. Comm 107945 (2021)
[2] A. van Roekeghem et al., High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites, Phys. Rev. X 6, 041061 (2016)
[3] A. van Roekeghem et al., High-throughput study of the static dielectric constant at high temperatures in oxide and fluoride cubic perovskites, Phys. Rev. Mat. 4, 113804 (2020)
[4] Q. N. Meier, N. Mingo and A. van Roekeghem, Finite temperature dielectric properties of KTaO from first-principles and machine learning: Phonon spectra, Barrett law, strain engineering and electrostriction, arXiv:2206.08296 (2022)
TimeMonday, June 2617:30 - 18:00 CEST
Event Type
Chemistry and Materials