Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons:A Message Passing Neural Network Approach

TL;DR

This paper introduces a message passing neural network that accurately predicts both the frequencies and intensities of IR spectra of hydrocarbons, enabling detailed analysis of molecular optical properties.

Contribution

It presents a novel CMPNN model capable of predicting IR molar absorptivities with DFT-level accuracy, extending spectral prediction to include intensities and peak positions.

Findings

The CMPNN accurately predicts IR spectra of hydrocarbons with up to ten carbons.

The model's predictions are comparable to those of the DMPNN.

Both models show similar effectiveness in spectral prediction for hydrocarbons.

Abstract

Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead. Here, we propose a graph-based communicative message passing neural network (CMPNN) algorithm that can predict both the peak positions and absolute intensities corresponding to density functional theory (DFT) calculated molar absorptivities in the IR domain. By modifying existing spectral loss functions, we show that our method is able to predict with DFT-accuracy level the IR molar absorptivities of a series of hydrocarbons containing up to ten carbon atoms and apply the model to a set of larger molecules. We also compare the predicted spectra with those generated by the direct message passing neural network (DMPNN). The results suggest that both algorithms demonstrate similar predictive capabilities for hydrocarbons, indicating that either model could be effectively used in future research on spectral prediction for such systems.