Computational methodologies have been critical to our understanding of defects at nanometer scales. These methodologies have been dominated by two classes: quantum mechanics (QM)-based methods and semiempirical/classical methods. The former, while accurate and versatile, are time consuming, while the latter are efficient but limited in versatility and transferability. Recently, machine learning (ML) methods have shown initial promise in bridging these two limitations due to their accuracy and flexibility. In this work, the true capability of ML methods is explored by simulating defects in platinum over several length/time scales. We compare our results with density functional theory (DFT) for atomic-level defect behavior and with experiments for nanolevel behavior. We also compare our predictions with several classical potentials. This work aims to showcase the length/time scales attainable using ML, as well as the complexity they are capable of capturing, demonstrating that these methodologies may be effectively used, in the future, to bridge experiments and QM methods.

中文翻译：

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使用力场的机器学习对铂中的缺陷现象进行多尺度建模

计算方法对于我们理解纳米尺度的缺陷至关重要。这些方法由两类主导：基于量子力学 (QM) 的方法和半经验/经典方法。前者虽然准确且通用，但很耗时，而后者效率高，但通用性和可转移性有限。最近，机器学习 (ML) 方法因其准确性和灵活性而在弥合这两个限制方面显示出初步前景。在这项工作中，通过在几个长度/时间尺度上模拟铂中的缺陷来探索 ML 方法的真正能力。我们将我们的结果与原子级缺陷行为的密度泛函理论 (DFT) 和纳米级行为的实验进行了比较。我们还将我们的预测与几个经典势进行比较。