Lack of crystalline order in amorphous alloys, commonly called metallic glasses (MGs), tends to make them harder and more wear-resistant than their crystalline counterparts. However, finding inexpensive MGs is daunting; finding one with enhanced wear resistance is a further challenge. Relying on machine learning (ML) predictions of MGs alone requires a highly precise model; however, incorporating high-throughput (HiTp) experiments into the search rapidly leads to higher performing materials even from moderately accurate models. Here, we exploit this synergy between ML predictions and HiTp experimentation to discover new hard and wear-resistant MGs in the Fe–Nb–B ternary material system. Several of the new alloys exhibit hardness greater than 25 GPa, which is over three times harder than hardened stainless steel and only surpassed by diamond and diamond-like carbon. This ability to use less than perfect ML predictions to successfully guide HiTp experiments, demonstrated here, is especially important for searching the vast Multi-Principal-Element-Alloy combinatorial space, which is still poorly understood theoretically and sparsely explored experimentally.

中文翻译：

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通过将机器学习与高通量实验相结合，发现异常坚硬和耐磨的金属玻璃

非晶合金（通常称为金属玻璃 (MGs)）中缺乏结晶顺序，往往会使它们比结晶合金更坚硬、更耐磨。然而，找到便宜的 MG 是一项艰巨的任务。寻找具有增强耐磨性的产品是另一个挑战。仅依靠 MG 的机器学习 (ML) 预测需要高度精确的模型；然而，将高通量 (HiTp) 实验纳入搜索中会迅速导致性能更高的材料，即使来自中等准确度的模型也是如此。在这里，我们利用 ML 预测和 HiTp 实验之间的这种协同作用，在 Fe-Nb-B 三元材料系统中发现新的硬质耐磨 MG。几种新合金的硬度大于 25 GPa，它的硬度是硬化不锈钢的三倍以上，仅次于金刚石和类金刚石碳。这种使用不太完美的 ML 预测来成功指导 HiTp 实验的能力（此处展示）对于搜索广阔的多主元素合金组合空间尤其重要，该空间在理论上仍然知之甚少，实验探索也很少。