Search Results for author: Philip Loche

Found 2 papers, 1 papers with code

PET-MAD, a universal interatomic potential for advanced materials modeling

1 code implementation18 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.

Diversity

Physics-inspired Equivariant Descriptors of Non-bonded Interactions

no code implementations25 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.

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