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LOCATION:Schwarzhorn
DTSTART;TZID=Europe/Stockholm:20230626T173000
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UID:submissions.pasc-conference.org_PASC23_sess117_msa288@linklings.com
SUMMARY:Lattice Anharmonicity in Solids: High-Throughput Screening and Mac
 hine Learning
DESCRIPTION:Minisymposium\n\nAmbroise van Roekeghem (Université Grenoble A
 lpes)\n\nDespite important breakthroughs in the last decade, the calculati
 on of temperature dependent properties of solids still remains a challengi
 ng task, especially in the vicinity of structural phase transitions [1]. I
 n this talk, I will show examples of computational high-throughput screeni
 ng of thermal [2] and dielectric [3] properties, focusing on the example o
 f 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 curren
 t capabilities of plain density functional theory [4].<br />[1] A. van Roe
 keghem, J. Carrete and N. Mingo, Quantum Self-Consistent Ab-Initio Lattice
  Dynamics, Comp. Phys. Comm 107945 (2021)<br />[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)<br />[3] A. van Roekeghem et al., High-throughput study of t
 he static dielectric constant at high temperatures in oxide and fluoride c
 ubic perovskites, Phys. Rev. Mat. 4, 113804 (2020)<br />[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 l
 aw, strain engineering and electrostriction, arXiv:2206.08296 (2022)\n\nDo
 main: Chemistry and Materials\n\nSession Chair: Pascal Boulet (Aix-Marseil
 le University)
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