Seminar by Florian Knoop: Temperature-dependent material simulations
© 2024 EPFL
Join us for a seminar by Florian Knoop on Tuesday, August 6, at 11:00 AM in MXF 312. Florian is currently a postdoc at the Linköping University in Sweden working on temperature-dependent materials simulations based on density functional theory. He'll be talking about how to use phonons to perform fast yet precise temperature-dependent materials simulations from first principles.
In his talk Florian will present a scheme that couples density functional theory (DFT) simulations, self-consistent phonon theory in the temperature-dependent effective potentials (TDEP) framework [1], and machine-learning potentials to perform ab initio materials simulations at finite temperature for a variety of observables at the cost of <1000 supercell calculations. These range from thermodynamic properties of superconducting high-pressure hydrides [2] to spectroscopic observables under realistic conditions [3]. In particular, he'll present a method to simulate Raman response beyond the harmonic approximation for simple materials like GaN up to complex perovskites like the lead-free chalcogenide Perovskite BaZrS3 which is considered for photovoltaic applications, and discuss how Raman spectroscopy and phonon theory can shed light on the microscopic mechanisms influencing its optoelectronic performance [4]. The scheme is accelerated by a recent equivariant neural network potential [5, 6]. This furthermore enables non-perturbative simulations of thermal transport via the Green-Kubo approach [6, 7], delivering the accuracy of its fully ab initio counterpart [8] at a fraction of the computational costs.
[1] FK et al., J. Open Source Softw 9, 6150 (2024); https://tdep-developers.github.io/tdep/
[2] D Laniel, F Trybel, B Winkler, FK, et al., Nat Commun 13, 6987 (2022)
[3] N. Benshalom, et al., Phys Rev Mater 6, 033607 (2022)
[4] K Ye, M Menahem, T Salzillo, FK, et al., arXiv 2402.18957; S. Filippone, et al., Phys Rev Mater 4, 091601 (2020)
[5] JT Frank, OT Unke, KR Müller, in Advances in Neural Information Processing Systems, Vol. 35 (2022), pp. 29400–29413
[6] MF Langer, JT Frank, FK, J. Chem. Phys. 159, 174105 (2023)
[7] MF Langer, FK, et al., Phys. Rev. B 108, L100302 (2023)
[8] FK, M Scheffler, C Carbogno, Phys Rev B 107, 224304 (2023); FK, et al., Phys Rev Lett 130, 236301 (2023)