Chiheb Ben Mahmoud

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Related publications (6)

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli, Sanggyu Chong

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Chiheb Ben Mahmoud

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

EPFL2023

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

Michele Ceriotti, Chiheb Ben Mahmoud, Nataliya Lopanitsyna

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindere ...

2021

Origins of structural and electronic transitions in disordered silicon

Michele Ceriotti, Chiheb Ben Mahmoud

2021

Learning the electronic density of states in condensed matter

Michele Ceriotti, Andrea Anelli, Chiheb Ben Mahmoud

AMER PHYSICAL SOC2020

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