36 citations · 53 across the 5 of their papers we have counts for
5 papers
Generative Nested Sampling of Atomistic Thermodynamic Landscapes
Alessandro Coretti, Nico Unglert, Sebastian Falkner +2
Nested sampling (NS) resolves the thermodynamics of an atomistic system from a single simulation, but its practical reach is limited by the Markov-chain updates needed to decorrela…
How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces
Linus C. Erhard, Johannes Schörghuber, Aleix Comas-Vives +1
Copper is a highly promising catalyst for the electrochemical CO reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as et…
Replica exchange nested sampling
Nico Unglert, Livia Bartók Pártay, Georg K. H. Madsen
Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Mar…
Quantitative predictions of the thermal conductivity in transition metal dichalcogenides: The impact of point defects in MoS and WS monolayers
Srinivisan Mahendran, Jesús Carrete, Andreas Isacsson +2
Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the ant…
Comparison of the Green-Kubo and homogeneous non-equilibrium molecular dynamics methods for calculating thermal conductivity
Bonny Dongre, Tao Wang, Georg K. H. Madsen
Different molecular dynamics methods like the direct method, the Green-Kubo (GK) method and homogeneous non-equilibrium molecular dynamics (HNEMD) method have been widely used to c…