高通量实验和数据驱动方法在金属玻璃中的应用

Weijie Xie, Weihua Wang, Yanhui Liu
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引用次数: 1

摘要

材料基因组工程(MGE)已成功应用于各个领域,产生了一系列性能优异的新型材料。在高通量模拟、实验和数据驱动技术方面取得了重大进展,使许多类别的材料能够有效预测、快速合成和表征。在这篇简短的综述中,我们介绍了使用MGE在金属玻璃(MGs)领域取得的成就,特别是高通量实验和数据驱动方法。高通量实验有助于在短时间内高效合成和表征许多材料,从而为数据驱动方法构建高质量的材料数据库。与机器学习相结合,可以揭示和预测具有所需性能的潜在合金。随着机器学习计算能力和算法的进步,有希望建立复杂的组成-结构-性质关系,从而有助于高效准确地预测新的MGs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On the application of high-throughput experimentation and data-driven approaches in metallic glasses

On the application of high-throughput experimentation and data-driven approaches in metallic glasses

Materials genome engineering (MGE) has been successfully applied in various fields, resulting in a series of novel materials with excellent performance. Significant progress has been made in high-throughput simulation, experimentation, and data-driven techniques, enabling the effective prediction, rapid synthesis, and characterization of many classes of materials. In this brief review, we introduce the achievements made in the field of metallic glasses (MGs) using MGE, in particular high-throughput experimentation and data-driven approaches. High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time, enabling the construction of high-quality material databases for data-driven methods. Paired with machine learning, potential alloys of desired properties may be revealed and predicted. Along with the progress in computational power and algorithms of machine learning, the complex composition-structure-properties relationship is hopefully established, which in turn help efficient and precise prediction of new MGs.

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