# Sim2Ls: FAIR simulation workflows and data

arXiv: https://arxiv.org/abs/2110.06886 · HTML: https://mlsanity.com/paper/2110.06886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves](https://mlsanity.com/paper/2601.06655/) (2026)
- [Materials Informatics: An Algorithmic Design Rule](https://mlsanity.com/paper/2305.03797/) (2023)
- [A collaborative digital twin built on FAIR data and compute infrastructure](https://mlsanity.com/paper/2507.00048/) (2025)

## Abstract (arXiv)

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.