Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials

Title data

Pracht, Philipp ; Pillai, Yuthika ; Kapil, Venkat ; Csányi, Gábor ; Gönnheimer, Nils ; Vondrák, Martin ; Margraf, Johannes T. ; Wales, David J.:
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials.
In: Journal of Chemical Theory and Computation. Vol. 20 (2024) Issue 24 . - pp. 10986-11004.
ISSN 1549-9626
DOI: https://doi.org/10.1021/acs.jctc.4c01157

Official URL:

Abstract in another language

Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building on previous comprehensive assessments of efficient methods for infrared (IR) spectroscopy, this study investigates the predictive accuracy and computational efficiency of gas-phase IR spectra calculations, accessible through a combination of modern semiempirical quantum mechanical and transferable machine learning potentials. A composite approach for IR spectra prediction based on the double-harmonic approximation, utilizing harmonic vibrational frequencies in combination squared derivatives of the molecular dipole moment, is employed. This approach allows for methodical flexibility in the calculation of IR intensities from molecular dipoles and the corresponding vibrational modes. Various methods are systematically tested to suggest a suitable protocol with an emphasis on computational efficiency. Among these methods, semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction are assessed across a diverse data set of organic molecules. We particularly focus on the recently reported foundational machine learning potential MACE-OFF23 to address the accuracy limitations of conventional low-cost quantum mechanical and force-field methods. This study aims to establish a standard for the efficient computational prediction of IR spectra, facilitating the rapid and reliable identification of unknown compounds and advancing automated high-throughput analytical workflows in chemistry.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry
Result of work at the UBT:	Yes
DDC Subjects:	500 Science > 540 Chemistry
Date Deposited:	13 Jan 2025 08:18
Last Modified:	13 Jan 2025 08:40
URI:	https://eref.uni-bayreuth.de/id/eprint/91541