688 citations · 1.4k across the 8 of their papers we have counts for
8 papers
Machine learning surrogate models for particle insertions and element substitutions
Ryosuke Jinnouchi
Two machine learning-aided thermodynamic integration schemes to compute the chemical potentials of atoms and molecules have been developed and compared. One is the particle inserti…
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…
Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks
Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi +2
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scr…
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…
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…
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…