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通过机器学习预测HAMA/GelMA混合水凝的风湿性质

Bincan Deng¹, Sibai Chen², Fernando López Lasaosa³

¹Department of Foundational Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.

Journal of the mechanical behavior of biomedical materials

|April 14, 2025

概括

这项研究开发了一种机器学习模型,用于预测氨酸甲基酸 (HAMA) /凝甲基酸 (GelMA) 水凝的粘度和剪切应力,帮助生物材料设计用于3D生物打印.

科学领域:

生物材料科学生物材料科学
类风病学类风病学类风病学
机器学习机器学习

背景情况:

对于成功的3D生物打印结构而言,rheological属性至关重要.
了解生物材料配方,质学和可打印性是进步的关键.

研究的目的:

开发一种机器学习模型,用于预测HAMA/GelMA水凝的风质性质.
创建一个可解释和热力学一致的生物材料设计预测模型.

主要方法:

在各种配方中测量了HAMA/GelMA水凝的风湿性质.
在生成的数据集上使用四个算法训练了一种机器学习模型.
开发了一种包含Redlich-Kister多项式的多层感知子模型 (HydroThermoMLP),以提高可解释性和热力学坚持性.

主要成果:

一个机器学习模型准确地预测了HAMA/GelMA水凝的粘度和剪切应力.
水热MLP模型在遵守热力学约束的情况下,显示了与随机森林相比的准确性.
该模型促进了概括,并为预测应用提供了信心.

结论:

热MLP模型是混合液凝粘度的一个强大的预测工具.
这种方法有助于指导生物材料设计,通过遵守基本的热力学准则.
该研究推动了3D生物打印应用的预测模型的开发.

关键词:

凝MA GelMA 是一个很好的方法.哈马哈马哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈混合水凝的混合水凝.机器学习是机器学习.雷德利希-基斯特多项式风病学性质风病学性质粘度粘度粘度粘度粘度

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