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Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations.[1] At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD).[2] This efficiency is jeopardized though, if a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.
To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO$_2$ and RuO$_2$, the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of ($1\times1$) surface unit-cells.[3] Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.
[1] A. P. Bartók, M. C. Payne, R. Kondor and G. Csányi, Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons, Phys. Rev. Lett. 104, 136403 (2010)
[2] V. L. Deringer, M. A. Caro and G. Csányi, Machine Learning Interatomic Potentials as Emerging Tools for Materials Science, Adv. Mater. 31, 1902765 (2019)
[3] J. Timmermann, F. Kraushofer, N. Resch, P. Li, Y. Wang, Z. Mao, M. Riva, Y. Lee, C. Staacke, M. Schmid, C. Scheurer, G. S. Parkinson, U. Diebold and K. Reuter, IrO$_2$ Surface Complexions Identified through Machine Learning and Surface Investigations, Phys. Rev. Lett. 125, 206101 (2020)
