278 citations · 761 across the 6 of their papers we have counts for
9 papers
Distinguishing sodium-ion penetration and sustained transport in realistic hard carbon nanostructures
Carolina Cruz-Cardona, Andreea Hedes, Patrick Rowe +7
Hard carbons are promising anodes for sodium-ion energy storage, yet the relationship between their heterogeneous microstructure and sodium transport remains poorly understood. Her…
Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick
Fabian L Thiemann, Christoph Schran, Patrick Rowe +2
Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort…
Defect-Dependent Corrugation in Graphene
Fabian L. Thiemann, Patrick Rowe, Andrea Zen +2
Graphene's intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulati…
Machine learning potentials for complex aqueous systems made simple
Christoph Schran, Fabian L. Thiemann, Patrick Rowe +3
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as soli…
Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling
Fabian L. Thiemann, Patrick Rowe, Erich A. Müller +1
We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on…
An Accurate and Transferable Machine Learning Potential for Carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto +2
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, nam…