6 papers
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…
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…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +5
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
Filippo Bigi, Joseph W. Abbott, Philip Loche +12
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational c…
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
Arslan Mazitov, Filippo Bigi, Matthias Kellner +6
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…
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…