Publications by Kevin Kazuki Huguenin-Dumittan

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

Computational methods at the atomic scale based on machine learning have been a fruitful research field in the past decade by merging the advantages of classical and quantum mechanical approaches. While parts of the field are already reaching a mature stat ...

EPFL2025

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Sanggyu Chong, Kevin Kazuki Huguenin-Dumittan, Federico Grasselli

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 electronic properties and therefore guide computational materials design. Given ...

2025

Fast and flexible long-range models for atomistic machine learning

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physic ...

2025

Completeness of Atomic Structure Representations

Michele Ceriotti, Sergey Pozdnyakov, Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

AIP Publishing2024

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotro ...

2024

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Amer Chemical Soc2023

A smooth basis for atomistic machine learning

Michele Ceriotti, Kevin Kazuki Huguenin-Dumittan, Filippo Bigi

Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to ...

AIP Publishing2022