Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Vibrational properties play a key role in determining the stability and thermal conductivity behaviour of materials. The quasi- harmonic approximation gives insight into the phononic properties of a compound, but in the established way, i.e. density functional theory based methods, it takes many calculation steps and consumes a lot of resources to arrive at the desired results. Machine learning (ML) trained interatomic potentials (e.g. Gaussian approximation potential, GAP) pose an alternative to the traditional computation way of phonons. We develop a Python code based workflow which combines automation tools like atomate2 with ML to ease providing interactomic potentials for (quantum chemical) computations and databases.