23 citations · 32 across the 3 of their papers we have counts for
3 papers
Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo Díaz Mirón +2
Photochemical processes govern phenomena ranging from solar energy conversion and atmospheric chemistry to vision and photosynthesis. Accurate simulation of these processes require…
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…