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MM: Fachverband Metall- und Materialphysik

MM 34: Data Driven Materials Science: Interatomic Potentials / Reduced Dimensions

MM 34.7: Vortrag

Donnerstag, 8. September 2022, 17:30–17:45, H45

Stability of binary precipitates in Cu-based alloys investigated through active learning and quantum computing — •Angel Diaz Carral¹, Xiang Xu², 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

Understanding the structure of thermodynamically stable precipitates is of great interest in material science as they can affect the electrical conductivity and mechanical properties of the matrix to a great degree. In this work, we use a relaxation-on-the-fly active learning algorithm in order to scan all possible binary candidates, for different types and concentrations of alloy elements (mainly Cu, Si, and Ni). Quantum-mechanical calculations are performed on a small number of candidates to train and improve the machine-learned potential. The model is then used to predict the enthalpy of formation of all candidates. The stability of binary precipitates, based on predicting the convex hull, is further assessed by the phonon density of states analysis calculated by classic and quantum computing.