Herein we establish formation ability descriptors of high-entropy rare-earth monosilicates (HEREMs) the data-driven discovery based on the high-throughput solid-state reaction and machine learning (ML) methods. Specifically, adequate high-quality data are generated with 132 samples synthesized by the self-developed high-throughput solid-state reaction apparatuses, and 30 potential descriptors are considered in ML simultaneously. Two classifications are proposed to study the phase formation of HEREMs the ML approach combined with the genetic algorithm: (Ⅰ) to distinguish pure HEREMs (X) from other phases and (Ⅱ) to categorize the detail phases of HEREMs (X2, X1, or X2+X1). Four formation ability descriptors (, , , and ) with a high validation accuracy (96.2%) are proposed as the optimal combination for Classification Ⅰ, where a smaller is determined to have the most significant influence on the formation of HEREMs. For Classification Ⅱ, a 100% validation accuracy is achieved by using only two formation ability descriptors ( and ), where the is analyzed to be the dominant feature and a lower is beneficial to the formation of X2-HEREMs. Based on our established formation ability descriptors, 6,045 unreported multicomponent silicates are explored, and 3,478 new HEREMs with 2,700 X2-and 423 X1-HEREMs are predicted.

中文翻译：

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数据驱动的高熵稀土单硅酸盐形成能力描述符的发现

在此，我们建立了高熵稀土单硅酸盐（HEREM）的形成能力描述符，这是基于高通量固态反应和机器学习（ML）方法的数据驱动发现。具体来说，通过自主研发的高通量固态反应装置合成的132个样本生成了足够的高质量数据，并且在ML中同时考虑了30个潜在的描述符。提出了两种分类来研究 HEREM 的相形成，即机器学习方法与遗传算法相结合：（Ⅰ）将纯 HEREM（X）与其他相区分开来；（Ⅱ）对 HEREM 的详细相进行分类（X2、X1 或X2+X1）。提出了具有高验证准确度（96.2％）的四个形成能力描述符（ ， ， ， 和 ）作为​​分类Ⅰ的最佳组合，其中较小的被确定为对 HEREM 的形成具有最显着的影响。对于分类Ⅱ，仅使用两个形成能力描述符（ 和 ）即可实现 100% 的验证准确度，其中 被分析为主导特征，较低的有利于 X2-HEREM 的形成。根据我们建立的形成能力描述符，探索了 6,045 种未报告的多组分硅酸盐，并预测了 3,478 个新 HEREM，其中包括 2,700 个 X2-HEREM 和 423 个 X1-HEREM。