Exploring inhomogeneous surfaces: Ti-rich SrTiO(110) reconstructions active learning

The investigation of inhomogeneous surfaces, where various local structures coexist, 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 SrTiO 3 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 inhomogeneous SrTiO 3 (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. The atomic configurations of the inhomogeneous surfaces are unraveled using an evolutionary strategy backed by a machine-learned neural-network force field. Excellent agreement with scanning tunneling microscopy images is demonstrated.