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RESEARCH PAPER · Advanced Science · 2023

A Comprehensive and Versatile Multimodal Deep‐Learning Approach for Predicting Diverse Properties of Advanced Materials

Shun Muroga, Yasuaki Miki, Kenji Hata

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A Comprehensive and Versatile Multimodal Deep‐Learning Approach for Predicting Diverse Properties of Advanced MaterialsOriginalMultimodal Deep Learning: An Emerging Technique Integrating Multiple Generative Models for Virtual Screening of Complex Materials0Multimodal Deep Learning for Digital Transformation of Polymer Composites1Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data4Development of Process Informatics for AI-driven Autonomous Polymer Processing：Application to Film Extrusion0Deep learning methods improve genomic prediction of wheat breeding42Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials10Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights25Single-Image-Based Deep Learning for Precise Atomic Defect Identification6Investigation of the accuracy of multi-input models using deep learning to estimate the tensile shear strength of adhesive joints1Applications of Materials Process Informatics : Multimodal Deep Learning and Autonomous Experiments0Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design1Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning12Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction4Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks15An advanced deep learning approach for energy absorption prediction in porous metals across diverse strain rate scenarios5Explainable Multimodal Machine Learning for revealing structure-property relationships in carbon nanotube fibers9Predicting mechanical properties of magnetorheological elastomers during the manufacturing process using a new machine learning method5Data-Driven Discovery of Multifunctional Organic Polymer Aerogels by Machine Learning-Assisted Large-Scale Property Screening4Multimodal Deep Learning for Digital Transformation of Fiber-reinforced Plastics01Chapter 1 Nanotechnology innovation: AIartificial intelligence-enhanced polymer drug delivery systems0From Industry 4.0 to 5.0: Exploring the Opportunity of Biodegradable Freshness Indicator Packaging16A microstructural design framework using deep convolution-generative adversarial networks for predicting the behavior of CNT-PDMS systems0A Wearable Electrochemical Biosensor for Salivary Detection of Periodontal Inflammation Biomarkers: Molecularly Imprinted Polymer Sensor with Deep Learning Integration29The role of computational materials science in achieving sustainable development goals: A review5A versatile multimodal learning framework bridging multiscale knowledge for material design21Artificial intelligence‐assisted atomic precision clusters for central nerve diseases0AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25Revealing the evolution of order in materials microstructures using multi-modal computer vision3Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions3High-throughput defect detection and coverage quantification of graphene grids via a dual-stage deep learning framework1AI awakens food packaging: Integrative advances, challenges, and future perspectives22Toward efficient dielectric material discovery: a comparative evaluation of Transformer and AutoML-based inverse learning1Computational Methods for Predicting Material Properties in Industry 4.00Advances in the Application of Machine Learning in Composite Materials5Materials Informatics: Emergence to Autonomous Discovery in the Age of AI15Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing13Atomically Precise Clusterzymes: A Programmable Optoelectronic Platform for Neuroscience3Combining multimodal fatigue fracture surface images for analysis with a CNN1A Dynamic‐Weighted Deep Transfer Learning Framework for Thermal Conductivity Prediction and Analysis2Advances in Generative Models for Accelerated Discovery of New Materials1Multidimensional correlation analysis between charcoal structure and microbial colonization toward functional biochar design0An Interpretable Multimodal Architecture for the Discovery of High-Efficiency Thermally Activated Delayed Fluorescence Emitters1Multimodal machine learning for integrating heterogeneous analytical systems0Development of Multimodal Deep Learning Technologies to Promote Green Transformation in Plastic Molding Processes0A multimodal machine learning approach for the integrated design of active magnetic regenerators with coordinated multilayer material layout and thermo-fluidic transfer characteristics0AI-driven property prediction and design optimisation of composite materials: A review0Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction0

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  2. Multimodal Deep Learning for Digital Transformation of Polymer Composites

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  3. Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data

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  4. Development of Process Informatics for AI-driven Autonomous Polymer Processing：Application to Film Extrusion

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  6. Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

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  7. Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights

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  8. Single-Image-Based Deep Learning for Precise Atomic Defect Identification

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  10. Applications of Materials Process Informatics : Multimodal Deep Learning and Autonomous Experiments

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  11. Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

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  14. Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks

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  16. Explainable Multimodal Machine Learning for revealing structure-property relationships in carbon nanotube fibers

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  17. Predicting mechanical properties of magnetorheological elastomers during the manufacturing process using a new machine learning method

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  20. 1Chapter 1 Nanotechnology innovation: AIartificial intelligence-enhanced polymer drug delivery systems

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