A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides
A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides
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DOI：
10.1016/j.mtadv.2023.100384
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发表时间：
2023-06
影响因子：
10
通讯作者：
Yi Je Cho;K. Lu
中科院分区：
文献类型：
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作者：
Yi Je Cho;K. Lu
聚合物衍生的碳氧化硅（SiOC）材料能够形成均匀的微观结构和高温稳定的性能。然而，工艺参数和组织/性能之间的关系尚未清楚地了解。本研究采用材料信息学方法对SiOC材料进行了分析和估计。数据集是根据以前报道的关于SiOC的文献结果构建的。相关性分析提供了相应的性能和微观结构的影响的工艺参数排名。这样的理解可以用于期望的材料制造。利用相关性分析得到的排序特征，提出了高精度的机器学习模型。此外，还讨论了数据收集、相关性分析和机器学习的要点以及当前数据集的局限性。所提出的SiOC材料的工作流程可以扩展到不同类型的聚合物衍生的陶瓷，通过将各种功能和目标涉及的处理变量，微观结构和性能。
Polymer-derived silicon oxycarbide (SiOC) materials enable the formation of homogeneous microstructures and high temperature stable properties. However, the relationships between the processing parameters and microstructures/properties have not been clearly understood. In this study, a materials informatics approach was employed to the SiOC materials to analyze and estimate the relationships. Datasets were constructed from results of previously reported literature about SiOC. The correlation analysis provided processing parameter ranking regarding the corresponding influences on the properties and microstructures. Such an understanding can be utilized for desired material fabrication. Machine learning models with high accuracy were proposed using the ranked features obtained from the correlation analysis. In addition, important points on the data collection, correlation analysis, and machine learning as well as limitations of the current dataset were discussed. The proposed workflow for the SiOC materials can be extended to different types of polymer-derived ceramics by incorporating various features and targets involved in the processing variables, microstructures, and properties.