Redox Potential Prediction of Fe(II)/Fe(III) Complexes: A Density Functional Theory and Graph Neural Network Approach

Key Points

• This research aims to develop a reliable computational method for predicting the redox potential of iron-based transition metal complexes.
• Combined tight-binding density functional theory (DFT) with standard DFT calculations.
• Utilized graph neural networks for enhanced prediction accuracy.
• Employed micro-solvated iron complex models.
• Achieved accurate predictions of redox potentials for iron complexes.
• Demonstrated the effectiveness of combining DFT with graph neural network approaches.