Dresden 2026 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 7: Mechanical Properties and Alloy Design II
MM 7.1: Talk
Monday, March 9, 2026, 15:45–16:00, SCH/A215
On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions — •Lukas Volkmer1, Leonardo Medrano Sandonas1, Philip Grimm2,3, Julia Kristin Hufenbach2,3, and Gianaurelio Cuniberti1,4 — 1Institute for Materials Science and Max Bergmann Center of Biomaterials,TUD Dresden University of Technology — 2Institute of Materials Science, Technische Universität Bergakademie Freiberg — 3Leibniz Institute for Solid State and Materials Research Dresden — 4Dresden Center for Computational Materials Science, TUD Dresden University of Technology
Aluminum-based alloys offer exceptional mechanical performance due to their low density, high specific strength, and strong resistance to oxidation and corrosion. In this work, we develop a scalable and transferable machine-learning interatomic potential (MLIP) capable of accurately predicting thermodynamic, mechanical, and microstructural properties across a broad concentration space of Al-Mg-Zr alloys. The MLIP is trained using an active-learning workflow that combines ab initio molecular dynamics, Bayesian uncertainty quantification, and kernel ridge regression, enabling efficient exploration of diverse atomic environments. Additionally, we model an Al/Al3Zr grain-boundary system using experimentally observed orientation relationships and calculate the stress-strain behavior. This framework provides a computationally efficient strategy for exploring the phase space of Al-based alloys and guiding the design of materials with optimized mechanical properties.
Keywords: Aluminum Alloys; Density Functional Theory; Molecular Dynamics; Elasticity