3 papers
physics.chem-ph2026
Simultaneous Learning of Static and Dynamic Charges
Philipp Stärk, Henrik StooÃ, 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…
physics.chem-ph2026
Learning Long-Range Representations with Equivariant Messages
Egor Rumiantsev, Marcel F. Langer, Tulga-Erdene Sodjargal +2
Machine learning interatomic potentials trained on first-principles reference data are becoming valuable tools for computational physics, biology, and chemistry. Equivariant messag…
physics.chem-ph2025
Fast and flexible long-range models for atomistic machine learning
Philip Loche, Kevin K. Huguenin-Dumittan, Melika Honarmand +5
Most atomistic machine learning (ML) models rely on a locality ansatz, and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitat…