Leveraging Active Learning-Enhanced Machine-Learned Interatomic Potential for Efficient Infrared Spectra Prediction
MCML Authors
Nitik Bhatia
Patrick Rinke
Prof. Dr.
Principal Investigator
Abstract
Nitik Bhatia
Patrick Rinke
Prof. Dr.
Principal Investigator
Abstract
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 situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework, implemented in the open-source software package PALIRS, for efficiently predicting the IR spectra of small catalytically relevant organic molecules. PALIRS leverages active learning to train a machine-learned interatomic potential, which is then used for machine learning-assisted molecular dynamics simulations to calculate IR spectra. PALIRS reproduces IR spectra computed with ab-initio molecular dynamics accurately at a fraction of the computational cost. PALIRS further agrees well with available experimental data not only for IR peak positions but also for their amplitudes. This advancement with PALIRS enables high-throughput prediction of IR spectra, facilitating the exploration of larger and more intricate catalytic systems and aiding the identification of novel reaction pathways.
misc
Preprint
Jun. 2025Authors
N. Bhatia • P. Rinke • O. KrejciLinks
Research Area
BibTeXKey: BRK25