Work

Machine learning potentials accurately reproduce vibrational dynamics in complex environments

Year: 2025

Type: article

Abstract: Vibrational spectroscopy provides a bond-specific view of molecular structure and dynamics, but translating spectroscopic observables to local environments requires the development of accurate models to compute spectroscopic observables from simulations. Recent advances in machine learning interatomic potentials (MLIPs) provide ab-initio-like accuracy at low computational cost, opening new opportunities for developing general, transferable models that can predict spectroscopic observables without system-specific parameterization. Here, we benchmark the performance of the Universal Model of Atoms (UMA), a recently developed MLIP, for predicting IR absorption spectra and picosecond frequency fluctuations of an ester carbonyl in a range of solvents. UMA results are compared with two established approaches: an empirical frequency map parameterized for the ester carbonyl and the semiempirical tight-binding method, GFN2-xTB. We find that UMA reproduces experimental observables with accuracy comparable to traditional methods, while offering broader generality and efficiency.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0306072

Authors: Chloe B. Starkey, Saptarsi Mondal, Carlos R. Baiz

Institution: The University of Texas at Austin

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: Welch Foundation, National Institutes of Health, National Institute of General Medical Sciences

Awards: G3250389790, G867376261, G9000234692