  Just like the scientific data they generate, simulation workflows for
research should be findable, accessible, interoperable, and reusable (FAIR).
However, while significant progress has been made towards FAIR data, the
majority of science and engineering workflows used in research remain poorly
documented and often unavailable, involving ad hoc scripts and manual steps,
hindering reproducibility and stifling progress. We introduce Sim2Ls
(pronounced simtools) and the Sim2L Python library that allow developers to
create and share end-to-end computational workflows with well-defined and
verified inputs and outputs. The Sim2L library makes Sim2Ls, their
requirements, and their services discoverable, verifies inputs and outputs, and
automatically stores results in a globally-accessible simulation cache and
results database. This simulation ecosystem is available in nanoHUB, an open
platform that also provides publication services for Sim2Ls, a computational
environment for developers and users, and the hardware to execute runs and
store results at no cost. We exemplify the use of Sim2Ls using two applications
and discuss best practices towards FAIR simulation workflows and associated
data.
