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This study presents a Deep Learning (DL)-based approach to predict gelation behavior in copolymer systems using compositional and physicochemical descriptors. Three architectures—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were tested and evaluated under conditions of pronounced class imbalance. The ANN model achieved the best performance, with an Accuracy (ACC) of 94% and an F1-score of 0.57, demonstrating strong discriminative capability in the binary classification of gelation propensity. To enhance robustness, threshold optimization was employed, and SHapley Additive exPlanations (SHAP) was used to identify key predictors, including specific monomer concentrations and degree of polymerization. The findings demonstrate that data-driven methods can effectively capture complex gelation patterns and provide interpretable, mechanistically relevant insights. This study underscores the potential of Artificial Intelligence (AI) to accelerate polymer design while reducing reliance on empirical experimentation.

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Predicting Gelation in Copolymers Using Deep Learning Through a Comparative Study of ANN, CNN, and LSTM Models with SHAP Explainability

  • 会议论文

  • 发表日期：

    2026年

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  • Selahattin Barış Çelebi,
  • Ammar Aslan,
  • Mutlu Canpolat

摘要

This study presents a Deep Learning (DL)-based approach to predict gelation behavior in copolymer systems using compositional and physicochemical descriptors. Three architectures—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were tested and evaluated under conditions of pronounced class imbalance. The ANN model achieved the best performance, with an Accuracy (ACC) of 94% and an F1-score of 0.57, demonstrating strong discriminative capability in the binary classification of gelation propensity. To enhance robustness, threshold optimization was employed, and SHapley Additive exPlanations (SHAP) was used to identify key predictors, including specific monomer concentrations and degree of polymerization. The findings demonstrate that data-driven methods can effectively capture complex gelation patterns and provide interpretable, mechanistically relevant insights. This study underscores the potential of Artificial Intelligence (AI) to accelerate polymer design while reducing reliance on empirical experimentation.