688 citations · 1.3k across the 3 of their papers we have counts for
4 papers
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
Carla Verdi, Ferenc Karsai, Peitao Liu +2
Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however…
- phase transition of zirconium predicted by on-the-fly machine-learned force field
Peitao Liu, Carla Verdi, Ferenc Karsai +1
The accurate prediction of solid-solid structural phase transitions at finite temperature is a challenging task, since the dynamics is so slow that direct simulations of the phase…
On-the-fly machine learning force field generation: Application to melting points
Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
An efficient and robust on-the-fly machine learning force field method is developed and integrated into an electronic-structure code. This method realizes automatic generation of m…
Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai +2
Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding ye…