23 citations · 41 across the 5 of their papers we have counts for
3 papers · 1 filter
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…