64 citations · 88 across the 8 of their papers we have counts for
4 papers · 1 filter
Unconstrained learning of equivariant atomistic properties
Joseph W. Abbott, Paolo Pegolo, Pol Febrer +6
Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, of…
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli +3
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with…
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…
Exploring the Absorption Spectrum of Simulated Water from MHz to the Infrared
Shane Carlson, Florian N. Brünig, Philip Loche +2
Absorption spectra of liquid water at 300 K are calculated from both classical and density functional theory molecular dynamics simulation data, which together span from 1 MHz to h…