TY - CONF A1 - Ertural, Christina A1 - Deringer, V. A1 - George, Janine T1 - Automation of machine learning driven interatomic potential generation for predicting vibrational properties N2 - 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. T2 - SALSA Make and Measure Conference: Interfaces CY - Berlin, Germany DA - 13.09.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics KW - Automated workflows PY - 2023 AN - OPUS4-58374 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -