23 citations · 57 across the 8 of their papers we have counts for
3 papers · 1 filter
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel Langer, Michele Ceriotti
The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational…
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…
Probing the effects of broken symmetries in machine learning
Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the…