SurfaceScience21 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

O: Fachverband Oberflächenphysik

O 71: Mini-Symposium: Machine learning applications in surface science I

O 71.5: Hauptvortrag

Mittwoch, 3. März 2021, 15:00–15:30, R1

Theory-informed Machine Learning for Surface and Interface Structure Reconstruction from Experimental Data — Eric Schwenker^1,2, Chaitanya Kolluru^1,3, Marcel Chlupsa¹, Arun Mannodi Kanakkithodi¹, Richard Hennig³, Pierre Darancet^1,2, and •Maria Chan^1,2 — ¹Argonne National Laboratory, Lemont, USA — ²Northwestern University, Evanston, USA — ³University of Florida, Gainsville, USA

Determining atomistic structure at surfaces and interfaces is challenging because metastable surfaces/interfaces are likely accessible under realistic conditions, rendering energy-only searches insufficient, and experimental data often give incomplete information. Therefore, neither theory nor experimental data alone is sufficient to determine these structures. In this talk, we will discuss how we use machine learning to combine experimental and theory-based data to determine surface and interface structures.