Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

Zhu, Jing-Ang; Jia, Yetong; Lei, Jincheng

doi:10.3390/math9212804

Cited by 19 publications

(9 citation statements)

References 51 publications

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“…On the one hand, the quick‐drying properties of carrageenan wraps can be enhanced by controlling the ratio of hydrogel generation. On the other hand, the toughness and barrier properties of the wraps are improved by retaining part of the hydrogel [83].…”

Section: Preparation Methods and Mechanization Of Carrageenan Wrapsmentioning

confidence: 99%

Progress of Carrageenan‐Based Films and Coatings for Food Packaging Applications

Wang,

Guo,

Guo

2024

Packag Technol Sci

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As a polysaccharide extracted from marine red algae, carrageenan has the advantages of biodegradability, nontoxicity and water solubility over petroleum‐based plastics for use as films and coatings for keeping food fresh. Aqueous solutions of carrageenan can be cast into films on plates and then made into food packaging bags, as well as coated directly onto food surfaces, all of which are referred to as ‘wraps’. To overcome the inherent limitations of carrageenan as a packaging material, physical and chemical modifications have been studied. The mechanical, thermal, barrier and antibacterial properties of modified carrageenan make it widely applicable for extending the shelf life of food products and monitoring its freshness. The research progress of carrageenan‐based wraps in recent years has been reviewed in terms of preparation methods, physicochemical properties, gelation mechanisms and applications, and the factors affecting the drying rate of them have been analysed. Finally, applications and future research directions of carrageenan are summarized and discussed to provide useful guidelines for further research.

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Section: Preparation Methods and Mechanization Of Carrageenan Wrapsmentioning

confidence: 99%

Progress of Carrageenan‐Based Films and Coatings for Food Packaging Applications

Wang,

Guo,

Guo

2024

Packag Technol Sci

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“…[224][225][226] To overcome the shortcomings of MD based simulations, such as computational cost, time required to complete one simulation, machine learning-based analysis of hydrogels are focus area of upcoming research. Zhu et al 227 have investigated the tensile behavior of single network PAAm hydrogels using deep learning and a 3D convolutional approach. PAAm hydrogels network was modeled using a self-avoiding walk (SAW) network model.…”

Section: Machine Learning Based Approachmentioning

confidence: 99%

Atomistic simulations of pristine and nanoparticle reinforced hydrogels: A review

Kumar

Parashar

2023

WIREs Comput Mol Sci

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Hydrogel is a three-dimensional cross-linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal-based nanofillers to improve their mechanical strength. Due to improved computational power, computational-based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)-based approach is emerging as a prospective technique for simulating neat and nanocomposite-based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite-based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite-based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels.

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“…Deep Neural Networks (DNNs) can predict hydrogel properties based on their molecular structures [83,94]. By training on a dataset of known hydrogel compositions and their corresponding properties, DNNs can learn complex relationships between molecular features and hydrogel behaviour.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review

Negut,

Bita

2023

Gels

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AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of AI and ML techniques in the realm of hydrogel development, highlighting their significance in enhancing the design, characterisation, and optimisation of hydrogels for diverse applications. We introduced the concept of AI train hydrogel design, underscoring its potential to decode intricate relationships between hydrogel compositions, structures, and properties from complex data sets. In this work, we outlined classical physical and chemical techniques in hydrogel design, setting the stage for AI/ML advancements. These methods provide a foundational understanding for the subsequent AI-driven innovations. Numerical and analytical methods empowered by AI/ML were also included. These computational tools enable predictive simulations of hydrogel behaviour under varying conditions, aiding in property customisation. We also emphasised AI’s impact, elucidating its role in rapid material discovery, precise property predictions, and optimal design. ML techniques like neural networks and support vector machines that expedite pattern recognition and predictive modelling using vast datasets, advancing hydrogel formulation discovery are also presented. AI and ML’s have a transformative influence on hydrogel design. AI and ML have revolutionised hydrogel design by expediting material discovery, optimising properties, reducing costs, and enabling precise customisation. These technologies have the potential to address pressing healthcare and biomedical challenges, offering innovative solutions for drug delivery, tissue engineering, wound healing, and more. By harmonising computational insights with classical techniques, researchers can unlock unprecedented hydrogel potentials, tailoring solutions for diverse applications.

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Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

Cited by 19 publications

References 51 publications

Progress of Carrageenan‐Based Films and Coatings for Food Packaging Applications

Progress of Carrageenan‐Based Films and Coatings for Food Packaging Applications

Atomistic simulations of pristine and nanoparticle reinforced hydrogels: A review

Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review

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