Publications by Chiheb Ben Mahmoud

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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 ...

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 ...

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 ...

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 ...

Chiheb Ben Mahmoud

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

Learning the electronic density of states in condensed matter

Robustness of Local Predictions in Atomistic Machine Learning Models

Predicting hot-electron free energies from ground-state data

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

Origins of structural and electronic transitions in disordered silicon