J. Lahnsteiner

activity

20162025

most citedPhase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference

646 citations · 945 across the 9 of their papers we have counts for

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Showing 2021Show all

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cond-mat.mtrl-sci2021★ 32 cited

Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX $_{3}$ (X=I, Br, Cl) perovskites

Menno Bokdam, Jonathan Lahnsteiner, D. D. Sarma

Two seemingly similar crystal structures of the low-temperature (~100 K) MAPbX $_{3}$ (X=I,Br,Cl) perovskites, but with different relative Methylammonium (MA) ordering, have appeared…

cond-mat.mtrl-sci2021★ 45 cited

Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr $_{3}$ a phonon-liquid with Cs rattlers

Jonathan Lahnsteiner, Menno Bokdam

The phonon dispersion relations of crystal lattices can often be well-described with the harmonic approximation. However, when the potential energy landscape exhibits more anharmon…

Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX3​ (X=I, Br, Cl) perovskites

Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr3​ a phonon-liquid with Cs rattlers

Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX $_{3}$ (X=I, Br, Cl) perovskites

Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr $_{3}$ a phonon-liquid with Cs rattlers