Negut Irina, Bita Bogdan
National Institute for Laser, Plasma and Radiation Physics, 409 Atomistilor Street, 077125 Magurele, Romania.
Faculty of Physics, University of Bucharest, 077125 Magurele, Romania.
Gels. 2023 Oct 25;9(11):845. doi: 10.3390/gels9110845.
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.
人工智能（AI）和机器学习（ML）已成为包括水凝胶设计在内的各个科学领域中的变革性工具。这项工作探索了人工智能和机器学习技术在水凝胶开发领域的整合，突出了它们在增强水凝胶设计、表征及优化以用于各种应用方面的重要性。我们引入了人工智能训练水凝胶设计的概念，强调其从复杂数据集中解码水凝胶组成、结构和性质之间复杂关系的潜力。在这项工作中，我们概述了水凝胶设计中的经典物理和化学技术，为人工智能/机器学习的进步奠定了基础。这些方法为后续由人工智能驱动的创新提供了基本理解。还包括了由人工智能/机器学习赋能的数值和分析方法。这些计算工具能够预测水凝胶在不同条件下的行为，有助于定制其性能。我们还强调了人工智能的影响，阐明了它在快速材料发现、精确性能预测和优化设计中的作用。还介绍了神经网络和支持向量机等机器学习技术，这些技术利用大量数据集加速模式识别和预测建模，推动水凝胶配方的发现。人工智能和机器学习对水凝胶设计具有变革性影响。人工智能和机器学习通过加速材料发现、优化性能、降低成本以及实现精确定制，彻底改变了水凝胶设计。这些技术有潜力应对紧迫的医疗保健和生物医学挑战，为药物递送、组织工程、伤口愈合等提供创新解决方案。通过将计算见解与经典技术相结合，研究人员可以释放水凝胶前所未有的潜力，为各种应用量身定制解决方案。
