Machine Learning Isotropic g Values of Radical Polymers | JoVE Visualize

Area of Science:

Computational chemistry
Materials science
Spectroscopy

Background:

Density functional theory (DFT) is used for calculating spectroscopic parameters but is computationally expensive for large systems like polymers.
Organic radical polymers are promising for applications such as batteries, requiring accurate structure-parameter correlations.
Electron paramagnetic resonance (EPR) spectroscopy provides experimental data on these materials.

Purpose of the Study:

To develop and evaluate a machine learning (ML) model for predicting isotropic g values (g_iso) of organic radical polymers.
To compare ML-predicted g_iso values with DFT-calculated and experimentally measured values.
To assess the ML model's ability to handle varying radical densities and molecular dynamics.

Main Methods:

Trained a regression tree-based ML model on DFT-calculated g_iso values for poly(2,2,6,6-tetramethylpiperidinyloxy-4-yl methacrylate) (PTMA) structures from molecular dynamics (MD) trajectories.
Compared ML-predicted g_iso values (g_iso^pred) with DFT-derived (g_iso^calc) and experimental EPR data.
Evaluated model performance on structures from a separate MD trajectory, assessing sensitivity to radical density and structural equilibration.

Main Results:

The ML model achieved mean deviations of approximately 0.0001 compared to DFT-calculated g_iso values.
The model demonstrated sensitivity to radical density, accurately predicting g_iso values even for densities not present in the training set.
The ML model successfully reproduced changes in g_iso along the MD trajectory, indicating sensitivity to polymer structure equilibration.

Conclusions:

Machine learning offers a computationally efficient alternative to DFT for predicting spectroscopic parameters in large organic radical polymer systems.
The developed ML model shows promise for accelerating the design and analysis of materials for applications like organic radical batteries.
The ML approach can provide insights into structure-property relationships by correlating dynamic structural changes with spectroscopic outputs.