Interatomic potentials are essential for studying fundamental mechanisms of deformation and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are far above the scales accessible to first-principles studies. Existing potentials for non-fcc metals and nearly all alloys are, however, not sufficiently quantitative for many crucial phenomena. Here, machine learning in the Behler-Parrinello Neural Network framework is used to create a broadly-applicable potential for pure hcp Magnesium (Mg). Lightweight Mg and its alloys are technologically important while presenting a diverse range of slip systems and crystal surfaces relevant to both plasticity and fracture that present a significant challenge for any potential. The machine learning potential is trained on first-principles DFT-computable metallurgically-relevant properties and is then shown to well-predict metallurgically-crucial dislocation and crack structures and competing phenomena. Extensive comparisons to an existing very good potential are made. These results demonstrate that a single machine learning potential can represent the wide scope of phenomena required for metallurgical studies. The DFT database is openly available for use in any other machine learning method. The method is naturally extendable to alloys, which are necessary for engineering applications but where ductility and fracture are controlled by complex atomic-scale mechanisms that are not well-predicted by existing potentials.

中文翻译：

---

冶金机器学习II。镁的神经网络潜力

原子间的电势对于研究金属和合金的变形和破坏的基本机理至关重要，因为相关的缺陷（位错，裂纹等）远远超出了第一性原理研究的范围。但是，对于许多关键现象，非fcc金属和几乎所有合金的现有电势还不够定量。在这里，使用Behler-Parrinello神经网络框架中的机器学习为纯hcp镁（Mg）创造了广泛应用的潜力。轻质的镁及其合金在技术上很重要，同时还提供了与塑性和断裂相关的各种滑动系统和晶体表面，这对任何潜力都提出了严峻的挑战。机器学习潜能在可与冶金学相关的第一性原理上得到训练，然后被证明可以很好地预测冶金关键的位错，裂纹结构和竞争现象。对现有的非常好的潜力进行了广泛的比较。这些结果表明，单一的机器学习潜力可以代表冶金研究所需的广泛现象。DFT数据库可公开用于任何其他机器学习方法。该方法自然可以扩展到合金，这对于工程应用是必需的，但是延性和断裂是由复杂的原子尺度机制控制的，而这些机制不能用现有的电位很好地预测。