23 citations · 41 across the 5 of their papers we have counts for
5 papers
Fast and flexible long-range models for atomistic machine learning
Philip Loche, Kevin K. Huguenin-Dumittan, Melika Honarmand +5
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 limitat…
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…
Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho +2
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of thes…
Physics-inspired Equivariant Descriptors of Non-bonded Interactions
Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran +1
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 sca…
A smooth basis for atomistic machine learning
Filippo Bigi, Kevin Huguenin-Dumittan, Michele Ceriotti +1
Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighbourhood of each atom in t…