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Predictive atomistic simulations are increasingly employed for data intensivehigh throughput studies that take advantage of constantly growing computationalresources. To handle the sheer number of individual calculations that areneeded in such studies, workflow management packages for atomistic simulationshave been developed for a rapidly growing user base. These packages arepredominantly designed to handle computationally heavy ab initio calculations,usually with a focus on data provenance and reproducibility. However, inrelated simulation communities, e.g. the developers of machine learninginteratomic potentials (MLIPs), the computational requirements are somewhatdifferent: the types, sizes, and numbers of computational tasks are morediverse, and therefore require additional ways of parallelization and local orremote execution for optimal efficiency. In this work, we present the atomisticsimulation and MLIP fitting workflow management package wfl and Python remoteexecution package ExPyRe to meet these requirements. With wfl and ExPyRe,versatile Atomic Simulation Environment based workflows that perform diverseprocedures can be written. This capability is based on a low-leveldeveloper-oriented framework, which can be utilized to construct high levelfunctionality for user-friendly programs. Such high level capabilities toautomate machine learning interatomic potential fitting procedures are alreadyincorporated in wfl, which we use to showcase its capabilities in this work. Webelieve that wfl fills an important niche in several growing simulationcommunities and will aid the development of efficient custom computationaltasks.

wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows