论文标题
基于机器学习的分类，解释和预测金属层间阶段
Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases
论文作者
论文摘要
由于其较大的组成空间，具有所需特性的高凝胶合金（HEA）的设计具有挑战性。尽管各种机器学习（ML）模型可以预测特定的HEA固态阶段（SS），但由于数据集有限和ML功能不足，预测高层金属间阶段（IM）的发育不足。本文介绍了功能工程辅助的ML模型，以实现详细的相分类和高精度。通过结合基于相的基于相的基于物理的特征，可以发现在随机森林（RF）和支持向量机（SVM）回归器上训练的ML模型能够将单个SS和Common IM（Sigma，Laves，Heusler和Sehusler和Erractory B2阶段）分类为80-94％的准确性。机器学习的功能还可以解释IM形成。此外，对RF，SVM和神经网络（NN）模型的效率进行了严格评估。使用NN模型训练数据集时，发现相分类精度会降低。通过合成86种新合金来验证模型预测的准确性。这种方法为指导HEA阶段设计提供了一个实用且强大的框架，尤其是对于技术上重要的IM阶段。
The design of high-entropy alloys (HEA) with desired properties is challenging due to their large compositional space. While various machine learning (ML) models can predict specific HEA solid-solution phases (SS), predicting high-entropy intermetallic phases (IM) is underdeveloped due to limited datasets and inadequate ML features. This paper introduces feature engineering-assisted ML models that achieve detailed phase classification and high accuracy. By combining phase-diagram-based and physics-based features, it is found that the ML models trained on the Random Forest (RF) and Support Vector Machine (SVM) regressors, are able to classify individual SS and common IM (Sigma, Laves, Heusler, and refractory B2 phases) with accuracies ranging from 80 - 94%. The machine-learned features also enable the interpretation of IM formation. Furthermore, the efficacies of the RF, SVM, and neural network (NN) models are critically evaluated. The phase classification accuracies are found to decrease upon utilizing the NN model to train the datasets. The accuracy of the model prediction is validated by synthesizing 86 new alloys. This approach provides a practical and robust framework for guiding HEA phase design, particularly for technologically significant IM phases.
