22 citations · 35 across the 10 of their papers we have counts for
3 papers · 2 filters
An interpretable molecular descriptor for machine learning predictions in atmospheric science
Linus Lind, Hilda Sandström, Patrick Rinke
The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are partic…
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Silvana Botti +2
Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that…
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Nitik Bhatia, Patrick Rinke, Ondrej Krejci
Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in…