Machine-Learning-Backed Evolutionary Exploration of Ti-rich SrTiO3(110) Surface Reconstructions

26 June 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Machine learning
Surface reconstruction

Supplementary materials

Supplementary Information: Machine-Learning-Backed Evolutionary Exploration of Ti-rich SrTiO3(110) Surface Reconstructions
Additional figures comparing simulation to STM measurements and further details on uncertainty estimation. Moreover, the algorithm and figures explaining founder generation, and information on overall model performance.


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