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MA: Fachverband Magnetismus

MA 39: SrTiO₃: A Versatile Material from Bulk Quantum Paraelectric to 2D Superconductor II (joint session TT/KFM/MA/O)

MA 39.9: Talk

Thursday, March 21, 2024, 17:15–17:30, H 0104

Machine-learning-backed evolutionary exploration of the SrTiO₃(110) surface phase diagram — •Ralf Wanzenböck¹, Florian Buchner¹, Michele Riva², Jesús Carrete^{3, 1}, and Georg K. H. Madsen¹ — ¹Institute of Materials Chemistry, TU Wien, A-1060 Vienna, Austria — ²Institute of Applied Physics, TU Wien, A-1040 Vienna, Austria — ³Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, 50009 Zaragoza, Spain

We use Clinamen2, a modern functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES), to gain insights into the lesser-known regions of the complex SrTiO₃(110) surface phase diagram. To speed up the process, we leverage the transferability of a neural-network force field (NNFF) implemented on top of the state-of-the-art JAX framework.

Starting from smaller reconstructions in well-explored phases, such as the 4× 1 surface reconstruction [Wanzenböck et al., Digit Discov 1, 703-710 (2022)], the NNFF is iteratively refined using an active learning workflow that relies on uncertainty estimation techniques [Carrete et al., J. Chem. Phys 158, 204801 (2023)]. We show how this workflow and the underlying uncertainty metric lead to a flexible NNFF, highlighted by the exploration of out-of-sample SrTiO₃(110)-(2× n) reconstructions.

Keywords: SrTiO3; surface reconstruction; machine learning; evolutionary search