58374 2023 eng lecture 0 -- -- -- Knowing phonon properties is beneficial for predicting low thermal conductivity thermoelectric materials. Employing DFT consumes lots of computational resources. Using ML-driven interatomic potentials (MLIP, e.g., GAP) opens up a faster route, but most potentials are specifically tailored to a certain compound. We aim to generalize the MLIP generation in a Python code-based workflow, combining automatic DFT runs with automated GAP fits. Automation enables easier tests, benchmarks, and validation. SALSA Make and Measure Conference: Interfaces Berlin, Germany 13.09.2023 15.09.2023 publish false true 0 0 Christina Ertural eng uncontrolled Interatomic potentials eng uncontrolled Machine learning eng uncontrolled Phonons eng uncontrolled Thermoelectrics eng uncontrolled Automated workflows Ingenieurwissenschaften und zugeordnete Tätigkeiten 6 Materialchemie Material Datei im Netzwerk der BAM verfügbar ("Closed Access") Präsentation 6.0 Abteilungsleitung und andere Materialdesign