23 citations · 57 across the 8 of their papers we have counts for
4 papers · 1 filter
Long-Range Machine Learning Interatomic Potentials for Defect Energetics in SrTiO
Yee Chit Wong, Marcel F. Langer, Reinhard J. Maurer +1
Machine learning interatomic potentials (MLIPs) have advanced rapidly in recent years, yet the majority of models remain semilocal in nature and neglect long-range electrostatic in…
Truncated automatic sparse differentiation for machine learning interatomic potentials
Marcel F. Langer, Adrian Hill, Michele Ceriotti
Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynam…
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Simultaneous Learning of Static and Dynamic Charges
Philipp Stärk, Henrik Stooß, Marcel F. Langer +4
Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machi…