High-Accuracy Polymer Property Detection via Pareto-Optimized SMILES-Based Deep Learning

Area of Science:

• Materials Science
• Computational Chemistry
• Polymer Informatics

Background:

• Conventional polymer design relies on intuition and experience, facing challenges with the vast design space and demand for novel materials.
• Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers a promising approach for accelerated materials design.
• Existing ML and DL methods show potential but require enhanced models for accurate polymer classification and property prediction.

Purpose of the Study:

• To design and develop an advanced AI model for polymer property classification using chemical structure inputs.
• To enhance polymer informatics by creating a scalable and domain-specific solution for predicting material properties.
• To improve upon existing methods by capturing complex chemical dependencies within polymer structures.

Main Methods:

• Development of the Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model.
• Integration of a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) for feature extraction and sequence modeling.
• Optimization of the 1DCNN-GRU model hyperparameters using the Pareto Optimization Algorithm (POA) for improved performance.

Main Results:

• The SMILES-PPDCPOA model achieved an average classification accuracy of 98.66% across eight polymer property classes.
• The model demonstrated high precision and recall metrics, indicating robust classification performance.
• SMILES-PPDCPOA exhibited superior computational efficiency, completing tasks in 4.97 seconds, outperforming established methods like GCN-LR and ECFP-NN.

Conclusions:

• The proposed SMILES-PPDCPOA model offers a novel and effective deep learning framework for polymer property classification.
• The integration of 1DCNN, GRU, and Pareto Optimization provides a scalable and accurate solution for polymer informatics.
• Experimental validation confirms the potential of SMILES-PPDCPOA as a promising approach for advancing materials science and engineering.