npj: 晶格弛豫调控高熵合金—机器学习势
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高熵合金（HEAs）是由近等摩尔比的四种或更多种元素组成的多组分合金，能结晶成原子种类随机分散的简单晶格结构，具有非凡的材料特性，如高深度强度的面心立方结构FeCoNiCrMn或耐高温的体心立方结构NbMoTaW的 HEAs，是下一代技术应用的潜在候选材料。由于合金性质和材料性能依赖于其化学有序的实际状态，因此高效预测化学有序化程度并识别这些合金中的有序-无序转变，成为该领域的重要研究内容之一。
来自德国马普所的Fritz Körmann和俄罗斯Skolkovo创新中心的Alexander Shapeev共同领导的团队，采用了一种称为低秩势(LRP)的、“针对晶格格点”的机器学习交互模型，它能包含弛豫效应，并准确地表示多组分体系中的相互作用。相互作用势通过DFT超胞计算训练，可以系统地考虑局部晶格畸变的影响。与以前的研究结果相反，他们发现，通过这种局部畸变，固溶体在室温下稳定，并转变为一种新发现的层状半有序亚稳态。这一结果突出了局部弛豫对于固溶体稳定的重要作用，与竞争有序构型相比，原子弛豫不受对称性的约束。原子间势的集合可以进一步分析预测的不确定性。因此，所提出的方法能够在整个温度范围内以高效地、精确地对多组分合金（包括HEAs）进行建模，并寻找迄今尚未发现的、新的多组分有序态。
该文近期发表于npj Computational Materials 5: 55 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
Tatiana Kostiuchenko, Fritz Körmann, Jörg Neugebauer & Alexander Shapeev
Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.
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