Patrick Rowe

Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling

A. Michaelides, F. Thiemann, Patrick Rowe, 2020, The Journal of Physical Chemistry C.

Development of a machine learning potential for graphene

Gábor Csányi, Dario Alfè, Angelos Michaelides, 2017, 1710.04187.

Structure-Dynamics Relation in Physically-Plausible Multi-Chromophore Systems.

A. Datta, A. Troisi, Patrick Rowe, 2017, The journal of physical chemistry letters.

Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick

A. Michaelides, Christoph Schran, F. Thiemann, 2022, ACS nano.

Machine learning potentials for complex aqueous systems made simple

A. Michaelides, O. Marsalek, Christoph Schran, 2021, Proceedings of the National Academy of Sciences.

Defect-Dependent Corrugation in Graphene.

A. Michaelides, Andrea Zen, F. Thiemann, 2021, Nano letters.

An accurate and transferable machine learning potential for carbon.

Volker L. Deringer, A. Michaelides, Gábor Csányi, 2020, The Journal of chemical physics.

Importance and Nature of Short-Range Excitonic Interactions in Light Harvesting Complexes and Organic Semiconductors.

A. Troisi, Patrick Rowe, Daniele Padula, 2017, Journal of chemical theory and computation.

Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

Cédric R. Weber, Patrick Rowe, Jean-Marc Leyssale, 2022, Carbon.