1 code implementation • 18 Mar 2025 • Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, Michele Ceriotti
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the effort.
no code implementations • 25 Aug 2023 • Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality.