Machine learning potential for the Cu-W system

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation. Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W interfaces, and exhibits physically-reasonable behavior for solid W/liquid Cu systems. As will be demonstrated in forthcoming work, this near-ab initio-accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites.

融合铜优异的热电性能与钨卓越的耐磨性和热稳定性，Cu-W纳米颗粒增强金属基复合材料及纳米多层膜（NMLs）正被广泛应用于钎焊填充材料和等离子体及辐射屏蔽材料。鉴于体心立方Cu与面心立方W之间的大晶格失配，这些系统拥有超越适用于从头计算方法的尺度范围的复杂界面，从而推动了化学精确性原子间势的发展。在此，基于Behler-Parrinello框架，利用精心挑选的培训数据集，捕捉冶金相关的局部原子环境，开发了一种适用于Cu-W的神经网络势（NNP）。Cu-W NNP能够准确预测（i）Cu和W的冶金性质（弹性、堆垛缺陷、位错、热力学行为），（ii）Cu-W金属间化合物和固溶体的能量和结构，以及（iii）一系列面心立方Cu/体心立方W界面，并在固体W/液态Cu系统中展现出合理的物理行为。正如后续工作中将要展示的，这种近似从头计算精确的NNP可用于理解涉及界面驱动过程和Cu-W复合材料性质的复杂现象。