15 citations · 38 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Long-range electrostatics in atomistic machine learning: a physical perspective
Federico Grasselli, Kevin Rossi, Stefano de Gironcoli +1
The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range…
Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe
Luka Benić, Federico Grasselli, Chiheb Ben Mahmoud +2
Understanding and controlling the charge density wave (CDW) phase diagram of transition metal dichalcogenides is a long-studied problem in condensed matter physics. However, due to…
Adaptive energy reference for machine-learning models of the electronic density of states
Wei Bin How, Sanggyu Chong, Federico Grasselli +2
The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and ele…
Thermal conductivity of LiPS solid electrolytes with ab initio accuracy
Davide Tisi, Federico Grasselli, Lorenzo Gigli +1
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been sc…
Mechanism of charge transport in lithium thiophosphate
Lorenzo Gigli, Davide Tisi, Federico Grasselli +1
Lithium ortho-thiophosphate (LiPS) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and…