探索非均质表面:通过主动学习探索富钛 SrTiO3(110) 表面重构的进化过程

Georg K. H., Madsen, Ralf, Wanzenböck, Esther, Heid, Michele, Riva, Giada, Franceschi, Alexander M., Imre, Jesús, Carrete, Ulrike, Diebold
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引用次数: 0

摘要

对各种局部结构共存的非均质表面进行研究,对于了解具有技术意义的界面至关重要,但这也带来了巨大的挑战。在这里,我们通过将扫描隧道显微镜和可转移神经网络力场与进化探索相结合,研究了 (110) 取向 SrTiO3 的 (2 × m) 富钛表面的原子构型。我们利用主动学习方法,根据不同配置的需要迭代扩展训练数据。我们仅在众所周知的小型重构上进行训练,就能推断出在异质 SrTiO3(110)-(2×m) 表面的不同区域所遇到的复杂多样的覆盖层。我们的机器学习方法生成了几种新的候选结构,与实验结果吻合,并通过密度泛函理论进行了验证。该方法可扩展到其他具有大量共存表面重构特征的复杂金属氧化物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Inhomogeneous Surfaces: Evolutionary Exploration of Ti-rich SrTiO3(110) Surface Reconstructions via Active Learning
The investigation of inhomogeneous surfaces, where various local structures co-exist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × m) Ti-rich surfaces at (110)-oriented SrTiO3 by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the heterogeneous SrTiO3(110)-(2×m) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. The approach could be extended to other complex metal oxides featuring large coexisting surface reconstructions.
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