781 citations · 781 across the 1 of their papers we have counts for
3 papers
Machine Learning Unifies the Modelling of Materials and Molecules
Albert P. Bartok, Sandip De, Carl Poelking +4
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and trans…
A universal preconditioner for simulating condensed phase materials
David Packwood, James Kermode, Letif Mones +5
We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of materials. Our…
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning
David Stephenson, James R Kermode, Duncan A Lockerby
We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient predict…