3 papers
physics.chem-ph2026
Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Patrick Rinke
Machine learning has transformed the field of atomistic simulations by enabling the development of interatomic potentials that are computationally efficient and highly accurate. Th…
physics.chem-ph2026
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…
physics.chem-ph2025
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…