59 citations · 146 across the 7 of their papers we have counts for
Showing physics.chem-phShow all
2 papers · 1 filter
physics.chem-ph2024★ 1 cited
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…
physics.chem-ph2021★ 10 cited
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…