使用机器学习对复合材料断裂性的预测建模.
Bruna S H Tonin1, Lucas E Kava2, Handially S Vilela3
1Dept. of Restorative Dentistry, Ribeirão Preto School of Dentistry, University of São Paulo, Ribeirão Preto, SP, Brazil.
概括
机器学习模型准确地预测了离子释放复合材料的断裂性. 像Random Forest和XGBoost这样的组合方法即使在训练数据有限的情况下也显示出可靠的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 生物材料工程 生物材料工程
背景情况:
- 预测离子释放树脂基复合材料的断裂性 (K1c) 对牙科材料的开发至关重要.
- 了解填料成分 (玻璃,二酸二水合物) 和转化程度对K1c的影响是必不可少的.
- 机器学习为模拟复杂的物质属性关系提供了一个有前途的方法.
研究的目的:
- 应用机器学习模型来预测试验性离子释放树脂基复合材料的断裂性 (K1c).
- 评估不同数据集大小对不同机器学习模型预测性能和可靠性的影响.
- 为此特定的材料属性预测任务确定最有效的机器学习算法.
主要方法:
- 从21种复合制剂中分析了234个K1c值,其中含有不同比例的玻璃和二酸二水合物 (DCPD).
- 转换度 (DC) 被列入预测变量.
- 四个机器学习模型 (惩罚性回归,随机森林,XGBoost,神经网络) 被训练并测试在不同大小的数据集上 (n=164,88,50用于训练;n=70用于测试).
主要成果:
- XGBoost和Random Forest在不同训练集大小中表现出最高的预测性能 (RMSE分别为0.120和0.123) 和稳定性.
- 处罚回归显示了捕捉复杂相互作用的能力有限 (RMSE 0.208).
- 神经网络对较小的数据集表现出高灵敏度,在较小的数据上训练时准确度明显降低 (RMSE 0.728与n=50).
结论:
- 基于集团的机器学习模型,特别是随机森林和XGBoost,对于预测复合K1c的有效.
- 这些模型甚至在相对较小的训练数据集中提供可靠的预测.
- 虽然有限的数据可以做出可接受的预测,但更大的数据集可以提高对离子释放树脂基复合材料的模型可靠性和概括性.
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