Berlin 2024 – scientific programme

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O: Fachverband Oberflächenphysik

O 57: New Methods: Theory

O 57.7: Talk

Wednesday, March 20, 2024, 17:45–18:00, MA 043

Surface segregation in high-entropy alloys from alchemical machine learning — •Arslan Mazitov and Michele Ceriotti — Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique bulk properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals to study the tendency of different elements to segregate at the surface of a HEA. We show that, thanks to the physically-inspired functional form of the model, a small amount of data is sufficient to extend a potential that was previously developed using exclusively crystalline bulk phases, so that it can also accurately model defective configurations and surfaces. We then present several computational studies of surface segregation, including both a simulation of a 25-element alloy, that provides a rough estimate of the relative surface propensity of the various elements, and targeted studies of CoCrFeMnNi and IrFeCoNiCu, which provide further validation of the model, and insights to guide the modeling and design of alloys for heterogeneous catalysis.

Keywords: Machine learning interatomic potentials; Molecular dynamics; Surface segregation; High-entropy alloys