论文标题
机器学习超级合金微化学和物理描述的蠕变强度
Machine learning superalloy microchemistry and creep strength from physical descriptors
论文作者
论文摘要
我们建议使用高斯过程回归的超合金属性建模元素不可吻合的描述符。此外，我们开发了一种校正方法，为多相合金中的微化学提供最佳，最多的物理预测。对于超合金微化学，微结构和强度特性，确认了模型在预测中的性能。在测试数据中包括新的，看不见的元素时，模型仍然给出良好的预测。基于组件的描述符，这种外推到新的化学空间是不可能的。
We propose an element-agnostic set of descriptors to model superalloy properties with Gaussian process regression. Furthermore, we develop a correction method to deliver the best and most physical predictions for microchemistry in multi-phase alloys. The models' performance in predictions is confirmed for superalloy microchemistry, microstructure, and strength properties. When including new, unseen elements in the test data, the models still give good predictions; such extrapolations into new chemical-space would be impossible with component-based descriptors.
