TY - GEN A1 - Ertural, Christina T1 - Automation of machine learning driven interatomic potential generation for predicting vibrational properties N2 - Investigating the phononic properties is beneficial for predicting low thermal conductivity thermoelectric materials.1–3 Employing density functional theory4 takes many calculation steps and consumes a lot of computational resources.5,6 Using machine learning driven interatomic potentials (MLIP, e.g., Gaussian approximation potential,8 GAP) opens up a faster route to phonons7 but in most cases, the potentials are specifically tailored for a certain compound. In this work, we automate the generation of such MLIPs in a Python code-based workflow, based on the automation tools atomate29 and pymatgen10 which combines the automatic DFT computations with the automated fitting of GAPs. Automation enables easier testing, benchmarking and validation.11 We aim to provide the workflow-generated potentials for storage in databases. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics PY - 2023 UR - https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/57932 AN - OPUS4-57932 AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany