Kevin Kazuki Huguenin-Dumittan

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Kevin Kazuki Huguenin-Dumittan

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Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

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

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

Completeness of Atomic Structure Representations

Fast and flexible long-range models for atomistic machine learning