14 citations · 14 across the 2 of their papers we have counts for
4 papers
Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations
Manuel Grumet, Takeru Miyagawa, Olivier Pittet +4
Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fas…
Representing spherical tensors with scalar-based machine-learning models
Michelangelo Domina, Filippo Bigi, Paolo Pegolo +1
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…
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
Divya Suman, Jigyasa Nigam, Sandra Saade +5
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…
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…