Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys.

IMPACT STATEMENT

By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys.

预测多组分合金，尤其是镍基高温合金的相沉淀是一项艰巨的任务。在这项工作中，我们通过整合高通量实验和机器学习算法，介绍了一种可靠有效的方法来建立镍基高温合金的成分与有害相之间的关系。快速获得了关于成分和相信息的8371套数据，并通过机器学习对其进行了分析，从而建立了高可信度的相预测模型。与传统方法相比，该方法在获取和分析实验数据方面具有明显的优势，也可以应用于其他多组分合金。

影响陈述

通过集成高通量实验和机器学习算法，希望有助于设计新的镍基高温合金，甚至其他多组分合金。