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

Thumbnail Image

Files

Leveraging_active_learning-enhanced_machine-learned_interatomic_potential_for_efficient_infrared_spectra_prediction.pdf (1.79 MB)

Access rights

openAccess
CC BY
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025-12

Department

Department of Applied Physics

Major/Subject

Mcode

Degree programme

Language

en

Pages

12

Series

npj Computational Materials, Volume 11, issue 1, pp. 1-12

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.

Description

| openaire: EC/HE/101073486/EU//EUSpecLab

Keywords

Other note

DOI

10.1038/s41524-025-01827-8

Citation

Bhatia, N, Rinke, P & Krejčí, O 2025, 'Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction', npj Computational Materials, vol. 11, no. 1, 324, pp. 1-12. https://doi.org/10.1038/s41524-025-01827-8

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202511128758

Collections

[article-cris] Perustieteiden korkeakoulu / SCI