59 citations · 146 across the 7 of their papers we have counts for
9 papers
Strain-Dependent Wetting of Graphene
Darren Wayne Lim, Xavier R. Advincula, William C. Witt +3
Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the con…
Introduction to machine learning potentials for atomistic simulations
Fabian L. Thiemann, Niamh O'Neill, Venkat Kapil +2
Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This pape…
On the increase of the melting temperature of water confined in one-dimensional nano-cavities
Flaviano Della Pia, Andrea Zen, Venkat Kapil +3
Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, ev…
Defects induce phase transition from dynamic to static rippling in graphene
Fabian L. Thiemann, Camille Scalliet, Erich A. Müller +1
Two-dimensional (2D) materials display nanoscale dynamic ripples that significantly impact their properties. Defects within the crystal lattice are the elementary building blocks t…
Classical quantum friction at water-carbon interfaces
Anna T. Bui, Fabian L. Thiemann, Angelos Michaelides +1
Friction at water-carbon interfaces remains a major puzzle with theories and simulations unable to explain experimental trends in nanoscale waterflow. A recent theoretical framewor…
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…