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

Aug 3, 2026

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Vibrational spectroscopy underpins molecular characterization and is well suited for computational study of molecular materials. This work evaluates predictive accuracy and computational efficiency for gas-phase infrared (IR) spectra using a composite workflow that combines the doubly-harmonic approximation with modern semiempirical quantum methods and transferable machine learning potentials. IR intensities are computed using harmonic vibrational frequencies together with squared derivatives of the molecular dipole moment and selected protocols. Extended tight-binding, charge equilibrium, and dipole-learning machine potentials are benchmarked on diverse organic datasets, with emphasis on MACE-OFF23.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages48

Reading Time2 h 1 min

AccessFree to download

Introduction

Infrared spectroscopy as a computational target

Static vs dynamical approaches

Doubly-harmonic approximation and efficiency

Semiempirical methods and machine learning potentials

Study scope and plug-in composite strategy

Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials Philipp Pracht,∗ ,†,‡ Yuthika Pillai,† Venkat Kapil,¶ ,†, § G´abor Cs´anyi, ∥ Nils G¨onnheimer,⊥ Martin Vondr´ak,⊥ Johannes T. Margraf,⊥ and David J. Wales† †Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom ‡Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States ¶Dept. of Physics and Astronomy, University College London, 17-19 Gordon St, London WC1H 0AH, UK §Thomas Young Centre & London Centre for Nanotechnology, 19 Gordon St, London WC1H 0AH, UK ∥Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom ⊥University of Bayreuth, Bavarian Center for Battery Technology (BayBatt), 95448 Bayreuth, Germany E-mail: [research@philipp-pracht.de](research@philipp-pracht.de) Abstract Keywords: IR spectroscopy, benchmark, machine learning potential, MACE, xTB Abstract 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 doubly-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 dataset 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. 1 Introduction Infrared (IR) spectroscopy remains a key analytical tool for characterizing molecular structures and dynamics, widely applied from fundamental research to industrial quality control. 1–3 Among the various types of vibrational spectroscopy, IR spectroscopy is arguably most widely adapted and has hence been a longstanding target for computational simulations, where the two most common approaches are Fourier transform-based spectra prediction from both classical and quantum dynamical simulations, 4 or static approaches based on the harmonic molecular Hessian and the so-called double-harmonic approximation (DHA) . 5 While the former approach includes anharmonic effects and an averaging over conformational states via time evolution of the system, the static approach generally produces less computational overhead. This computational efficiency can be especially important, since predicting vibrational spectra often involves time-consuming first-principles calculations that account specifically for electronic structure. To partially circumvent these problems, several approaches have been proposed to predict vibrational spectra from dynamical simulations via machine learning. 6–11 Within static ap

FAQs

What computational strategy is used to predict IR spectra in this study?

The approach combines the doubly-harmonic approximation with a composite workflow that uses harmonic vibrational frequencies and squared derivatives of the molecular dipole moment to obtain IR intensities.

Which model families are compared for IR spectra prediction?

The study systematically benchmarks semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction.

Why does the study emphasize MACE-OFF23?

MACE-OFF23 is used to address accuracy limitations of conventional low-cost quantum mechanical and force-field methods within the proposed efficient IR prediction framework.

Aug 3, 2026

Upload by Oliver