688 citations · 1.5k across the 10 of their papers we have counts for
7 papers · 1 filter
Efficient periodic density functional theory calculations of charged molecules and surfaces using Coulomb kernel truncation
Sudarshan Vijay, Martin Schlipf, Henrique Miranda +4
Density functional theory (DFT) calculations of charged molecules and surfaces are critical to applications in electro-catalysis, energy materials and related fields of materials s…
Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
Peitao Liu, Jiantao Wang, Noah Avargues +5
Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges t…
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 Transitions of Zirconia: Machine-Learned Force Fields Beyond Density Functional Theory
Peitao Liu, Carla Verdi, Ferenc Karsai +1
We present an approach to generate machine-learned force fields (MLFF) with beyond density functional theory (DFT) accuracy. Our approach combines on-the-fly active learning and $Δ…
- 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…