通过材料信息学预测商业和实验酸盐水泥的pH值
Clarice Ferreira Sabino1, Anastasiia Grymak2, Nikolaos Silikas3
1Faculty of Dentistry, National University of Singapore, Singapore.
概括
一个新的机器学习模型使用早期pH读数准确地预测了基于酸盐的水泥 (CSC) 的长期pH值. 这项创新简化了生物材料的测试,并有助于开发先进的内牙材料.
科学领域:
- 生物材料科学
- 材料工程
- 计算化学
背景情况:
- 基于酸盐的水泥 (CSCs) 由于其性而在牙科中至关重要.
- 准确预测它们的长期pH值对于材料开发和临床应用至关重要.
- 目前评估pH演变的方法耗时且资源密集.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测CSC的长期pH概况.
- 用早期的pH测量 (3小时和24小时) 来进行这些预测.
- 减少CSC体外测试的持续时间和规模.
主要方法:
- 在体外研究 (2014-2024) 中提取的pH和离子释放数据.
- 开发了一种混合堆叠的整体ML模型,集成了梯度增强回归器和顺序多层感知器.
- 使用不同表面积的商业和实验CSC的实验数据验证了该模型.
主要成果:
- ML模型显示出高预测精度 (R2值分别为0. 91,0. 89和0. 85在72,168和672小时).
- 实验验证证了预测和实际pH值之间的强相关性 (R2 > 0. 80).
- 该模型在不同的CSC配方和样本大小中显示出一致的性能.
结论:
- 使用早期的pH数据和样本表面积,开发的ML模型有效地预测了CSC的性演变.
- 这种方法显著减少了长时间测试和广泛样本使用的需要.
- 该模型支持加速生物材料开发和下一代内牙材料的设计.
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