688 citations · 1.5k across the 10 of their papers we have counts for
10 papers
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…
Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simul…
Machine Learning-Aided First-Principles Calculations of Redox Potentials
Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate…
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…
Nonmagnetic and ferromagnetic fcc cerium studied with one-electron methods
Fabien Tran, Ferenc Karsai, Peter Blaha
Density functional theory was used to study the nonmagnetic (NM) and ferromagnetic (FM) phases of face-centered cubic cerium. Functionals of four levels of approximations for the e…