Parameter studies for interatomic potentials using LAMMPS and pyiron

The pyiron framework is an abstraction layer to orchestrate parameter studies for atomistic simulation, covering both empirical interatomic simulations as well as ab-initio methods like density functional theory (DFT). Based on a generic interface atomistic structures can be seamlessly transferred between simulation codes to enable the coupling of different levels of theory all within the same simulation protocol developed in the python programming language. For two selected examples we demonstrate how this data-driven approach provides new scientific insights: First for the fitting of an interatomic machine learning potential, the dependence of the hyperparameters cut-off radius and number of descriptors is systematically analyzed by coupling LAMMPS and FitSNAP inside pyiron. By optimizing these hyperparameters the computational efficiency can be improved by nearly one order of magnitude while maintaining the same precision. Second the melting temperatures of interatomic potentials from the NIST database are calculated with the interface method and compared to experimental measurements. This highlights the importance of including finite temperature properties in the fitting process of interatomic potentials. In summary, the pyiron framework enables the rapid- prototyping and up-scaling of simulation protocols which couple multiple LAMMPS calculation to gain new scientific insights.