22 citations · 24 across the 4 of their papers we have counts for
4 papers
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…
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…
Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO to Methanol Conversion
Prajwal Pisal, Ondrej Krejci, Patrick Rinke
Transforming CO into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining hig…