Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 11; p. 3320
• Main Authors: Liu, Yang, Guo, Lanting, Hu, Xiaoyu, Zhou, Mengjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2025; MDPI
• Subjects: Analysis; Artificial intelligence; biodegradable materials; Business performance management; Composite materials; Datasets; Design; Food; Food packaging; Food quality; generative adversarial networks; graph neural networks; inverse materials design; Liquors; Machine learning; Materials science; Neural networks; Packaging; Packaging industry; Porous materials; sensor integration; Sensors; Sustainable agriculture; Sustainable development; sustainable food packaging
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25113320

This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end design from desired performance metrics to material composition. We developed a GAN-driven inverse design architecture specifically optimized for food packaging applications, integrating sensor-derived data on critical constraints such as biodegradability and barrier properties directly into the generative process. This integration occurs at three levels: (1) sensor-measured properties define conditioning targets for the GAN, (2) sensor data train the property prediction network, and (3) sensor-based characterization validates generated materials. An enhanced EquiformerV2 graph neural network was employed to accurately predict the formation energy, stability, and sensor-measurable properties of candidate materials. The model achieved a mean absolute error of 12 meV/atom for formation energy on the OMat24 test set (25% improvement over baseline models), while predictions of sensor-measured functional properties reached R2 values of 0.84–0.89 through the integration of experimental measurements and physics-based proxy models. The framework successfully generated over 100 theoretically viable candidate materials, with 20% exhibiting superior barrier properties and controlled degradation characteristics. Our computational approach demonstrated a 20–100× acceleration in screening efficiency compared to traditional DFT calculations while maintaining high accuracy. This work presents a significant advancement in computational materials discovery for sustainable packaging applications, offering a promising pathway to address the urgent global challenges of food waste and plastic pollution.