Search Results for author:
Found 3 papers, 1 papers with code
Representing spherical tensors with scalar-based machine-learning models
Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects -- from atoms to the macroscopic scale -- transform under the action of rigid rotations.
PET-MAD, a universal interatomic potential for advanced materials modeling
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.
Oxidation states, Thouless' pumps, and non-trivial ionic transport in non-stoichiometric electrolytes
We finally examine the impact of our findings on the transport properties of non-stoichiometric alkali-halide melts, where the same topological conditions that would induce a quantum pump mechanism along certain closed loops in configuration space also determine a non-trivial transport regime such that most of the total charge current results to be uncorrelated from the ionic ones.
Materials Science Other Condensed Matter Chemical Physics Computational Physics
