6 papers
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…
Tracking the Lithiation State of LiSi from Machine-Learned XPS Binding Energies
Michael Alejandro Hernandez Bertran, Davide Tisi, Federico Grasselli +3
X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often h…
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…
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…
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…
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…