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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MDPI AG
2025-05-01
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| author | Yang Liu Lanting Guo Xiaoyu Hu Mengjie Zhou |
| author_facet | Yang Liu Lanting Guo Xiaoyu Hu Mengjie Zhou |
| author_sort | Yang Liu |
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| description | 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. |
| format | Article |
| id | doaj-art-2ca931e1fcf04157b28ea4c3e05e498b |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-2ca931e1fcf04157b28ea4c3e05e498b2025-08-20T02:23:09ZengMDPI AGSensors1424-82202025-05-012511332010.3390/s25113320Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial NetworksYang Liu0Lanting Guo1Xiaoyu Hu2Mengjie Zhou3Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USAThe Department of Food Science and Human Nutrition, University of Illinois Urbana-Champaign, Champaign, IL 61801, USADepartment of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030, USADepartment of Computer Science, University of Bristol, Bristol BS8 1QU, UKThis 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.https://www.mdpi.com/1424-8220/25/11/3320inverse materials designgenerative adversarial networkssustainable food packagingbiodegradable materialsgraph neural networkssensor integration |
| spellingShingle | Yang Liu Lanting Guo Xiaoyu Hu Mengjie Zhou Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks Sensors inverse materials design generative adversarial networks sustainable food packaging biodegradable materials graph neural networks sensor integration |
| title | Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks |
| title_full | Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks |
| title_fullStr | Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks |
| title_full_unstemmed | Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks |
| title_short | Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks |
| title_sort | sensor integrated inverse design of sustainable food packaging materials via generative adversarial networks |
| topic | inverse materials design generative adversarial networks sustainable food packaging biodegradable materials graph neural networks sensor integration |
| url | https://www.mdpi.com/1424-8220/25/11/3320 |
| work_keys_str_mv | AT yangliu sensorintegratedinversedesignofsustainablefoodpackagingmaterialsviagenerativeadversarialnetworks AT lantingguo sensorintegratedinversedesignofsustainablefoodpackagingmaterialsviagenerativeadversarialnetworks AT xiaoyuhu sensorintegratedinversedesignofsustainablefoodpackagingmaterialsviagenerativeadversarialnetworks AT mengjiezhou sensorintegratedinversedesignofsustainablefoodpackagingmaterialsviagenerativeadversarialnetworks |