Unlocking the Potential of Machine Learning in Enhancing Quantum Chemical Calculations for Infrared Spectral Prediction

Abstract

Infrared (IR) spectroscopy is a fundamental tool for analyzing molecular structures and chemical interactions by identifying the vibrational modes of molecules. Traditional quantum mechanical methods, such as density functional theory, are highly accurate but computationally expensive and impractical for large-scale molecular systems. This project investigates the integration of machine learning (ML) techniques to predict IR spectra, offering a promising alternative that significantly reduces computational costs while maintaining high accuracy. Additionally, the project explores the utilization of IR spectra for molecular identification and classification into molecular families, enhancing the practical utility of spectral data in various scientific applications. Using TensorFlow-based ML frameworks, models were developed and trained on a data set derived from high-quality computational chemistry analyzers. These data sets, sourced from computationally optimized geometry and IR spectrum from the Gaussian 16 Program Suite, include extensive molecular geometry data, bond lengths, vibrational modes, and other quantum mechanical properties. The models aim to predict key IR spectral features, such as vibrational frequencies and intensities, while maintaining interpretability by linking chemical and quantum mechanical principles to predictions. The integration of ML with IR spectroscopy provides a scalable as well as accelerated solution for analyzing complex molecular systems. This approach holds potential in fields such as drug discovery, materials science, and chemical engineering, where rapid and accurate spectral predictions are critical. This perspective highlights the advancements achieved, the current challenges, and the future potential of ML in the context of IR spectroscopy, providing a solid foundation for further exploration at the intersection of chemistry and data science.