203 citations · 216 across the 18 of their papers we have counts for
7 papers · 1 filter
Conservative adaptive-precision interatomic potentials
David Immel, Ralf Drautz, Godehard Sutmann
Adaptive precision molecular dynamics simulations have developed along energy- and force-coupling approaches, which allow for a continuous transition between different particle des…
AI-Driven Expansion and Application of the Alexandria Database
Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck +9
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stabili…
Graph atomic cluster expansion for foundational machine learning interatomic potentials
Yury Lysogorskiy, Anton Bochkarev, Ralf Drautz
Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We intro…
Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography
Mariano Forti, Alesya Malakhova, Yury Lysogorskiy +5
Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly chal…
Efficient local atomic cluster expansion for BaTiO close to equilibrium
Anna Grünebohm, Matous Mrovec, Maxim N. Popov +4
Barium titanate (BTO) is a representative perovskite oxide that undergoes three first-order ferroelectric phase transitions related to exceptional functional properties. In this wo…
Nanoindentation simulations for copper and tungsten with adaptive-precision potentials
David Immel, Matous Mrovec, Ralf Drautz +1
We perform nanoindentation simulations for both the prototypical face-centered cubic metal copper and the body-centered cubic metal tungsten with a new adaptive-precision descripti…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…