  Predictive atomistic simulations are increasingly employed for data intensive
high throughput studies that take advantage of constantly growing computational
resources. To handle the sheer number of individual calculations that are
needed in such studies, workflow management packages for atomistic simulations
have been developed for a rapidly growing user base. These packages are
predominantly designed to handle computationally heavy ab initio calculations,
usually with a focus on data provenance and reproducibility. However, in
related simulation communities, e.g. the developers of machine learning
interatomic potentials (MLIPs), the computational requirements are somewhat
different: the types, sizes, and numbers of computational tasks are more
diverse, and therefore require additional ways of parallelization and local or
remote execution for optimal efficiency. In this work, we present the atomistic
simulation and MLIP fitting workflow management package wfl and Python remote
execution package ExPyRe to meet these requirements. With wfl and ExPyRe,
versatile Atomic Simulation Environment based workflows that perform diverse
procedures can be written. This capability is based on a low-level
developer-oriented framework, which can be utilized to construct high level
functionality for user-friendly programs. Such high level capabilities to
automate machine learning interatomic potential fitting procedures are already
incorporated in wfl, which we use to showcase its capabilities in this work. We
believe that wfl fills an important niche in several growing simulation
communities and will aid the development of efficient custom computational
tasks.
