Driven Design and Optimization of Smart Materials for Advanced Chemical Engineering Applications

Abstract

Artificial intelligence (AI) is revolutionizing the design and optimization of smart materials for chemical engineering applications by fast predicting, designing and optimizing adaptive materials. Smart materials adapt intelligently to thermal, chemical, mechanical, electrical, magnetic or optical stimuli. These materials can be used in catalysis, separations, sensing, energy conversion and environmental remediation. Existing methods rely primarily on expensive and time-consuming trial-and-error experiments. Limited, imbalanced and noisy data and black box predictions limit the reliability, interpretability and sustainable decision making. Therefore, to tackle these issues, a Hybrid Interpretable Optimization Decomposition Neural Network (HIOD-NN) framework has been proposed for predicting, interpreting and multi-objective optimization of material properties. The Conditional Tabular Generative Adversarial Network (GAN) generates realistic synthetic samples the Physics-Guided Neural Network (PGNN) predicts material properties with scientific constraints and Local Interpretable Model-Agnostic Explanations (LIME) find influential composition and processing features. Particle Swarm Optimization (PSO) finds optimal synthesis parameters, whereas the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) balances performance, cost, energy consumption, toxicity, scalability and sustainability. Feedback from the experiments keeps improving the dataset and subsequent predictions and optimizations. The overall performance of HIOD-NN resulted in accuracy of 95.8%, R² of 0.96, RMSE of 0.061 and 2.18% experimental error. Thus, HIOD-NN explains clear reliable and sustainable way for fast-paced smart materials discovery and implementation in industry.