Title: On-the-fly machine learning force fields with near first principles precision: Predicting phase transitions in complex Dynamic Solids

Speaker: Menno Bokdam (UT, MESA+)
Time: March 31, 2022, 10:00–11:00
Location: Hybrid: TU/e (Flux 1.124) and online (MS Teams)

Abstract: Lattice dynamics at the atomic scale is often well described by phonons in the harmonic approximation. However, it does not always suffice. For example, it does not explain a crystal’s phase transitions or why some materials have ultra-low thermal conductivity. While a more accurate method, ab-initio molecular-dynamics (MD), captures the anharmonicity of the atomic interactions correctly, it is computationally orders of magnitude too expensive to describe the effects of phonon scattering related to the "rattling" and "flipping" of atoms and molecules. In this talk I will present a recently developed method that can account for these effects based on an on-the-fly Machine- Learning Force-Field (MLFF) approach [1]. It allows us to automatically ‘train’ smooth and ‘cheap’ models of the potential energy surface based on density functional theory calculations. The MLFF gives access to the nanosecond time- and tens of nanometer length-scales and opens up the possibility to predict complex phase transitions, capture the formation and breaking of weak bonds, ion diffusion and simulate lattice thermal conductivity in complex ‘Dynamic Solids’. It enables linking to experiments such as NMR dipolar coupling [2] and momentum resolved phonon spectroscopy [3]. I will illustrate the capabilities of the approach with several examples from the halide perovskites.

 

1. Jinnouchi, Lahnsteiner, Karsai, Kresse and Bokdam, Phase transitions of hybrid perovskites simulated by machine- learning force fields trained on the fly with Bayesian inference, Phys. Rev. Lett. 122, 225701 (2019)
2. Grueninger, Bokdam, Leupold, Tinnemans, Moos, de Wijs, Panzer and Kentgens, Microscopic (dis)order and dynamics of cations in mixed FA/MA lead halide perovskites, J. Phys. Chem. C, 125, 1742-1753 (2021)
3. Lahnsteiner and Bokdam, Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr₃ a phonon liquid with Cs rattlers, Phys. Rev. B 105, 024302 (2022)

The CCER seminars are aimed at researchers interested in computational approaches to (energy) research. The seminar is small-scale, typically 15 participants, and interactive, offering lots of room for discussion. If you would like to attend, just This email address is being protected from spambots. You need JavaScript enabled to view it..