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 . In this talk, I will show examples of computational high-throughput screening of thermal  and dielectric  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  or to go beyond the current capabilities of plain density functional theory .
 A. van Roekeghem, J. Carrete and N. Mingo, Quantum Self-Consistent Ab-Initio Lattice Dynamics, Comp. Phys. Comm 107945 (2021)
 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)
 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)
 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