Exploring the Structure-Energy Landscape of Oxidic Structures on Copper Surfaces with Machine Learning Potentials and Global Optimization Methods

Copper has established itself as a transcending material in many key technologies [1]. Despite its long history as an essential metal, there are still uncharted areas of research that have not been systematically conducted. The full-scale structure determination of thin film Cu_xO/Cu(111) surfaces is one such example. The Cu(111) surface is the most stable surface of copper. Therefore, it will be essential to clarify the most relevant surface structure of its oxidized thin film state under targeted synthesis/technical conditions. Although, there have been many articles from both experimental and theoretical investigations that have discovered the most representative surface oxidic structures such as the “8”, “29”, and “44” surface oxides (which covers 8, 29, and 44 times the area of <i>p</i>(1 x 1) Cu(111) surface unit cell, respectively) [2,3], it is still unclear whether these “well-known” thin film structures capture the full family of surface oxides on Cu(111). The objective of this work is to provide a systematic and through survey of the potential energy landscape of O/Cu(111) by utilizing with global optimization technique (such as the Global Optimization with First-principles Energy Expression, GOFEE) [4] coupled to a Gaussian Approximation Potential (GAP) [5] which utilizes descriptors that distinguish local atomic environments within a structure. Through this work, we hope to guide experiments in characterizing new (meta)stable surface oxides on Cu(111) and provide accurate atomistic models for further studies in niche applications such as corrosion and energy-related catalysis.<br/><br/>[1] S. J. Kim, S. Kim, J. Lee, Y. Jo, Y.-S. Seo, M. Lee, Y. Lee, C. R. Cho, J.-P. Kim, M. Cheon, J. Hwang, Y. I. Kim, Y.-H. Kim, Y.-M. Kim, A. Soon, M. Choi, W. S. Choi, S.-Y. Jeong, and Y. H. Lee, <i>Adv. Mater.</i> <b>33</b>, 2007345 (2021).<br/>[2] Y.-J. Lee, T. T. Ly, T. Lee, K. Palotás, S. Y. Jeong, J. Kim, and A. Soon, <i>Appl. Surf. Sci.</i> <b>562</b>, 150148 (2021).<br/>[3] N. A. Richter, C.-E. Kim, C. Stampfl, and A. Soon, <i>Phys. Chem. Chem. Phys.</i> <b>16</b>, 26735 (2014).<br/>[4] M. K. Bisbo and B. Hammer, <i>Phys. Rev. Lett.</i> <b>124</b>, 086102 (2020).<br/>[5] A. P. Bartók and G. Csányi, <i>J. Quantum Chem.</i> <b>115</b>, 1051 (2015).