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O: Fachverband Oberflächenphysik
O 54: Focus Session: Molecular Nanostructures on surfaces - New Concepts towards Complex Architectures III
O 54.2: Vortrag
Mittwoch, 14. März 2018, 11:00–11:15, MA 004
Artificial Intelligence for Molecules on Surfaces — •Milica Todorović1, Michael U. Gutmann2, Jukka Corander3,4, and Patrick Rinke1 — 1Aalto University, Espoo, Finland — 2University of Edinburgh, Edinburgh, UK — 3University of Oslo, Oslo, Norway — 4University of Helsinki, Helsinki, Finland
Quantum mechanical accuracy is required to simulate the structures and properties of molecules on surfaces. However, accurate calculations are costly and extensive sampling is prohibitive, so studies into molecular assembly and surface-supported processes like diffusion are guided by human intuition. To promote unbiased studies into molecular surface structures and phenomena, we have combined atomistic simulations with Bayesian optimisation - an artificial intelligence (AI) technique designed for complicated optimisation tasks [1]. We demonstrate how the AI was adapted to learn surface and property landscapes of molecules on surface with minimal computational sampling [2], delivering most stable surface structures with favorable designer properties. Energy landscapes can be further data-mined for low energy paths and associated trajectories to reveal the atomistic mechanisms behind key processes. We showcase the capability of AI to infer complex properties on several examples of atomic and molecular surface adsorbates. [1] M.U. Gutmann and J. Corander, J. Mach. Learn. Res. 17, 1 (2016). [2] M. Todorović, M. U. Gutmann, J. Corander and P. Rinke, arXiv:1708.09274 (2017).