Ingenieurwissenschaften und zugeordnete Tätigkeiten
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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.