With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method-dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method-sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path-dependent and that current physiochemical theories find challenging to predict.

中文翻译：

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通过反复进行机器学习和高通量实验来加快发现金属玻璃的速度。

元素周期表中有一百多种元素，存在着许多潜在的新材料，可以应对我们今天面临的技术和社会挑战。然而，在没有任何指导的情况下，在巨大的组合空间中进行搜索非常缓慢且昂贵，特别是对于受加工影响较大的材料。我们根据先前报告的观察结果，来自理化理论的参数训练机器学习（ML）模型，并使其依赖于合成方法，以指导高通量（HiTp）实验，以在Co-V-Zr中找到新的金属玻璃系统三元。实验观察与模型的预测非常吻合，但是所预测的精确组成之间存在定量差异。我们使用这些差异来重新训练ML模型。改进后的模型不仅大大提高了Co-V-Zr系统的准确性，而且在所有其他可用的验证数据中也大大提高了准确性。然后，我们使用改进的模型来指导在另外两个以前未报告的三元组中发现金属玻璃。尽管我们的ML和HiTp实验迭代使用方法指导我们快速发现了三种新的玻璃形成系统，但它也为我们提供了定量精确，对合成方法敏感的金属玻璃预测器，可提高使用性能，从而提高性能有望大大加快许多新型金属玻璃的发现。