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

TL;DR

This paper introduces PALIRS, an active learning framework that trains machine-learned interatomic potentials to efficiently predict IR spectra with high accuracy, reducing computational costs significantly.

Contribution

The work presents a novel active learning-based method for training interatomic potentials to accurately predict IR spectra, enabling high-throughput and scalable simulations.

Findings

PALIRS reproduces IR spectra with ab-initio accuracy

PALIRS achieves significant reduction in computational cost

PALIRS shows good agreement with experimental IR data

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.