Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, hence, various theoretical approaches have been developed to simulate predict IR spectra. However, based on quantum calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy semiempirical methods orders magnitude faster than DFT). Here, we introduce new approach, universal machine learning (ML) models AIQM series targeting CCSD(T)/CBS level, that can deliver spectra with close DFT (compared experiment) speed GFN2-xTB method. This approach harmonic oscillator approximation frequency scaling factors fitted experimental data. While benchmarks reported here are focused spectra, our implementation supports anharmonic simulations via dynamics VPT2. These implementations available in MLatom as described https://github.com/dralgroup/mlatom be performed online web browser.

Language: Английский

The evolution of machine learning potentials for molecules, reactions and materials

Chemical Society Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This review offers a comprehensive overview of the development machine learning potentials for molecules, reactions, and materials over past two decades, evolving from traditional models to state-of-the-art.

Language: Английский