22 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Efficient dataset generation for machine learning perovskite alloys
Henrietta Homm, Jarno Laakso, Patrick Rinke
Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed th…
Design Rules for Optimizing Quaternary Mixed-Metal Chalcohalides
Pascal Henkel, Jingrui Li, Patrick Rinke
Quaternary mixed-metal M(II)2M(III)Ch2X3 chalcohalides are an emerging material class for photovoltaic absorbers that combines the beneficial optoelectronic properties of lead-base…