3 papers
cond-mat.mtrl-sci2026
Machine Learning Potential-Driven Molecular Dynamics Simulations of Dehydrogenation in Pristine and Doped MgH
Bo Han, Jianchuan Wang, Rui Zhang +6
Machine learning potential-driven molecular dynamics simulations (ML-MD) were employed to provide atomistic insights into the dehydrogenation kinetics of pristine and doped MgH2. T…
cond-mat.mtrl-sci2024
Revealing trends in catalytic activity of adatoms for hydrogen adsorption on carbon: a case study of graphene and carbon nanotube
Thomas Leiner, David Holec
The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics o…
cond-mat.mtrl-sci2024
Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride
Ganesh Kumar Nayak, Prashanth Srinivasan, Juraj Todt +3
Amorphous silicon nitride (a-SiN) is a material which has found wide application due to its excellent mechanical and electrical properties. Despite the significant effort devoted i…