Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks
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 materi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3320 |
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| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 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. |
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| ISSN: | 1424-8220 |