Machine learning strategies for high-entropy alloys
Machine learning strategies for high-entropy alloys
复制标题
高熵合金的机器学习策略
DOI：
10.1063/5.0030367
复制
发表时间：
2020-12
影响因子：
3.2
通讯作者：
J. Rickman;Ganesh Balasubramanian;Christopher J. Marvel;Helen M. Chan;M. Burton
中科院分区：
文献类型：
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作者：
J. Rickman;Ganesh Balasubramanian;Christopher J. Marvel;Helen M. Chan;M. Burton
近年来，高熵（HE）合金的研究取得了显著的增长，因为在某些情况下，这些系统可以表现出优异的性能，包括增强的抗氧化性、上级机械性能和期望的磁性能。然而，由于可以从可用的元素调色板制造非常大量的不同系统，因此有前途的HE合金的识别极具挑战性。出于这个原因，已经采用机器学习策略来减小相关联的化学/组成空间的大小。在这篇综述中，我们概述了几种计算策略，导致了有用的合金的识别，并讨论了这些方法的相对优点和缺点。我们还提出了简短的教程，说明使用选定的计算方法，他的表征和设计。
The study of high-entropy (HE) alloys has seen dramatic growth in recent years as, in some cases, these systems can exhibit exceptional properties, including enhanced oxidation resistance, superior mechanical properties, and desirable magnetic properties. The identification of promising HE alloys is, however, extremely challenging due to the extraordinarily large number of distinct systems that may be fabricated from the available palette of elements. For this reason, machine learning strategies have been employed to reduce the size of the associated chemistry/composition space. In this review, we outline several computational strategies that have led to the identification of useful alloys and discuss the relative merits and shortcomings of these approaches. We also present short tutorials illustrating the use of selected computational approaches to HE characterization and design.