MACE4IR: A Foundation Model for Molecular Infrared Spectroscopy

MCML Authors

Nitik Bhatia

→ Group Patrick Rinke
 AI-based Material Science

Patrick Rinke

Prof. Dr.

Principal Investigator

AI-based Material Science

Abstract

Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs capable of handling a wide range of elements and their combinations has limited their broader applicability. In this work, we introduce MACE4IR, a machine learning foundation model built on the MACE architecture and trained on 10 million geometries and corresponding density-functional theory (DFT) energies, forces and dipole moments from the QCML dataset. The training data encompasses approximately 80 elements and a diverse set of molecules, including organic compounds, inorganic species, and metal complexes. MACE4IR accurately predicts energies, forces, dipole moments, and infrared spectra at significantly reduced computational cost compared to DFT. By combining generality, accuracy, and efficiency, MACE4IR opens the door to rapid and reliable infrared spectra prediction for complex systems across chemistry, biology, and materials science.

misc

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Preprint

Aug. 2025

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Authors

N. Bhatia • O. Krejci • S. Botti • P. Rinke • M. A. L. Marques

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Links

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Research Area

 C3 | Physics and Geo Sciences