66 citations · 109 across the 8 of their papers we have counts for
5 papers · 1 filter
Comparing the latent features of universal machine-learning interatomic potentials
Sofiia Chorna, Davide Tisi, Cesare Malosso +3
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface ac…
Mechanistic study of mixed lithium halides solid state electrolytes
Davide Tisi, Sergey Pozdnyakov, Michele Ceriotti
Lithium halides with the general formula LiMX, where M indicates metal ions and X halide anions are very actively studied as solid-state electrolytes, because of relati…
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…
Reconstructions and Dynamics of -Lithium Thiophosphate Surfaces
Hanna Türk, Davide Tisi, Michele Ceriotti
Lithium thiophosphate (LPS) is a promising solid electrolyte for next-generation lithium-ion batteries due to its superior energy storage, high ionic conductivity, and low-flammabi…
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…