SKM 2021 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 5: Topical Session Interface Dominated Phenomena - Poster

MM 5.13: Poster

Tuesday, September 28, 2021, 10:00–12:45, P

Revealing highly stable copper based alloys using active learning — •Angel Diaz Carral¹, Azade Yazdan Yar¹, Siegfried Schmauder², and Maria Fyta¹ — ¹Institute for Computational Physics (ICP), Universität Stuttgart, Allmandring 3, 70569, Stuttgart, Germany — ²Institut für Materialprüfung, Werkstoffkunde und Festigkeitslehre (IMWF), Pfaffenwaldring 32 70569, Stuttgart, Germany

Copper based alloys, due to their high electrical conductivity and high strength, are of great importance for electric and electronic applications such as connectors or lead frames. To this end, we investigate the stability of Cu-Ni-Si-Cr alloys, that is copper alloys with nickel, silicon and chromium impurities. Through computational means, we scan a large number of impurities' concentration and configurations. A relaxation-on-the-fly active learning algorithm is applied in order to investigate the influence of this scanning and reveal the alloys of higher stability. The latter are used at a next step in larger scale simulations in order assist the design of alloys with pre-selected properties. Here, we mainly focus on the first part, the learning process and the stable alloy structures. We discuss the efficiency of this approach, the predictions that can be made and the impact in designing alloys.