Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi‐component systems from a high‐dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi‐component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1‐ω)(Ba_0.61Ca_0.28Sr_0.11TiO₃)‐ω(BaTi_0.888Zr_0.0616Sn_0.0028Hf_0.0476O₃) with a relatively high transition temperature and triple point, as well as the NiTi‐based pseudo‐binary phase diagram (1‐ω)(Ti_0.309Ni_0.485Hf_0.20Zr_0.006)‐ω(Ti_0.309Ni_0.485Hf_0.07Zr_0.068Nb_0.068) designed for high transition temperature (ω ⩽ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods.

中文翻译：

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使用主动学习确定具有所需特征的多组分相图

在这里，我们演示了如何根据现有的小型实验数据，从涉及多个元素的所有可能相图的高维虚拟空间中预测和实验验证多组分系统的相图。已知系统的本体相的实验数据代表来自该空间的采样，并且筛选该空间允许构建具有给定设计标准的多组分相图。这种方法使用机器学习方法来预测相图和贝叶斯实验设计，以最大限度地减少细化和验证的实验，所有这些都在主动学习循环中进行。通过预测和合成具有相对较高转变温度和三相点的铁电陶瓷体系(1- ω )(Ba _0.61 Ca _0.28 Sr _0.11 TiO ₃ )- ω (BaTi _0.888 Zr _0.0616 Sn _0.0028 Hf _0.0476 O ₃ )证明了该方法的有效性。 ，以及针对高转变温度 ( ω ⩽ _{)设计的 NiTi 基伪二元相图 (1-} ω )(Ti _0.309 Ni _0.485 Hf 0.20 Zr _0.006 )- ω (Ti _0.309 Ni _0.485 Hf _0.07 Zr _0.068 Nb _0.068 ) 1）。每个相图仅通过三个新实验进行验证和优化。这些化合物的复杂性超出了当今计算方法的能力范围。