23 citations · 32 across the 3 of their papers we have counts for
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physics.chem-ph2026
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…
physics.chem-ph2024★ 23 cited
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…