Machine learning-assisted design of the molecular structure of p-phenylenediamine antioxidants†
Abstract
This study employed machine learning to predict the solubility parameter (δ) and bond dissociation energy (BDE) of antioxidant molecules, focusing on p-phenylenediamine derivatives with varying carbon chain lengths, side group positions, and functional groups (–CH3, –OH, and –NH2). The multilayer perceptron (MLP) model, enhanced by data augmentation and genetic algorithms, was developed to correlate the “molecular structure–descriptor–target parameter” relationship. The model achieved high prediction accuracy (coefficient of determination >0.86, relative percent difference >2.62). SHapley Additive exPlanations analysis revealed molecular polarity as the key factor influencing antioxidant performance. Molecules with –NH2 side groups exhibited lower BDE values. A p-phenylenediamine derivative with ‘CH3[CH2]13CH(NH2)–’ connected to an aniline group showed optimal properties (Δδ = 0.02 (J cm−3)0.5, BDE = 289.46 kJ mol−1). Molecular simulations confirmed that the proposed antioxidant has excellent compatibility, anti-migration, and antioxidant activity in triglyceride oil. This study demonstrates the utility of MLP models for designing high-efficiency antioxidants for edible oils.