MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy

TL;DR

MACE4IRmol is an ensemble-based, uncertainty-aware foundation model trained on extensive molecular data, enabling accurate, efficient infrared spectra prediction across diverse chemical systems with reliable uncertainty estimates.

Contribution

This work introduces MACE4IRmol, a novel ensemble foundation model that provides accurate infrared spectra predictions and uncertainty quantification for a wide range of molecules, including inorganic and metal complexes.

Findings

Accurately predicts infrared spectra with DFT-level fidelity.

Provides reliable uncertainty estimates for diverse molecular systems.

Reduces computational cost compared to traditional DFT methods.

Abstract

Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that simultaneously span broad chemical diversity and provide reliable uncertainty estimates has limited their wider applicability. In this work, we introduce MACE4IRmol, an uncertainty-aware foundation model ensemble built on the MACE architecture. MACE4IRmol is trained on ~16 million molecular geometries and the 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 and inorganic compounds, and metal complexes. Importantly, MACE4IRmol is formulated as an ensemble of models to enable uncertainty quantification, which helps improve robustness in chemically diverse systems. Within this ensemble, separate models are trained with and without explicit dispersion corrections, allowing systematic assessment of van der Waals effects. In addition, MACE4IRmol delivers accurate predictions of energies, forces, dipole moments, and infrared spectra at a fraction of the computational cost of DFT, while enabling the explicit inclusion of nuclear quantum effects in infrared spectrum simulations. By combining generality, accuracy, efficiency, and uncertainty estimation, MACE4IRmol opens the door to rapid and reliable infrared spectra prediction for complex and diverse molecular systems.