通过经典势引导采样与自动化构型重构建模金属中的广延缺陷
Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction
AI总结：
本文提出经验势引导采样与自动化构型重构方法，构建可用于第一性原理计算的缺陷基因组，并以体心立方钨验证其能提升MLIP对广延缺陷和塑性机制的建模能力。
摘要：
位错网络和一般晶界等扩展缺陷在金属中普遍存在，准确建模这些广延缺陷对于理解其变形机制至关重要。现有的机器学习原子间势（MLIP）往往不足以充分描述这些缺陷，因为其显著的特征尺寸超出了第一性原理计算的计算极限。在本研究中，我们通过经验原子间势引导的采样建立全面的缺陷基因组，以应对这些挑战。为进一步能够对该缺陷基因组进行准确的第一性原理计算，我们开发了一种自动化构型重构技术。该方法利用巨正则蒙特卡洛模拟，通过精确插入原子，将缺陷原子团簇转化为周期性构型。这些策略使得能够开发用于建模金属中广延缺陷的高精度、可迁移MLIP。以体心立方钨作为模型体系，我们开发了一种MLIP，并在纳米压痕模拟中揭示了独特的塑性机制。该框架不仅提高了晶体材料中广延缺陷的建模精度，还通过战略性利用缺陷基因组，为进一步推进MLIP开发奠定了坚实基础。
英文摘要：
Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurately modeling these extensive defects is crucial for understanding their deformation mechanisms. Existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their significant characteristic sizes exceed the computational limits of first-principles calculations. In this study, we address these challenges by establishing a comprehensive defect genome through empirical interatomic potential-guided sampling. To further enable accurate first-principles calculations on this defect genome, we have developed an automated configuration reconstruction technique. This method transforms defect atomic clusters into periodic configurations through precise atom insertion, utilizing Grand Canonical Monte Carlo simulations. These strategies enable the development of highly accurate and transferable MLIPs for modeling extensive defects in metals. Using body-centered cubic tungsten as a model system, we develop an MLIP that reveals unique plastic mechanisms in simulations of nanoindentation. This framework not only improves the modeling accuracy of extensive defects in crystalline materials but also establishes a robust foundation for further advancement of MLIP development through the strategic use of defect genomes.
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