Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.

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

SUBMITTER: Bhatia N 

PROVIDER: S-EPMC12571885 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature