Dispersion-corrected Machine Learning Potentials for 2D van der Waals Materials

Mikkel Ohm Sauer, Peder Meisner Lyngby, Kristian Sommer Thygesen

Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio…