  Data-driven (DD) interatomic potentials (IPs) trained on large collections of
first principles calculations are rapidly becoming essential tools in the
fields of computational materials science and chemistry for performing
atomic-scale simulations. Despite this, apart from a few notable exceptions,
there is a distinct lack of well-organized, public datasets in common formats
available for use with IP development. This deficiency precludes the research
community from implementing widespread benchmarking, which is essential for
gaining insight into model performance and transferability, and also limits the
development of more general, or even universal, IPs. To address this issue, we
introduce the ColabFit Exchange, the first database providing open access to a
large collection of systematically organized datasets from multiple domains
that is especially designed for IP development. The ColabFit Exchange is
publicly available at \url{https://colabfit.org/}, providing a web-based
interface for exploring, downloading, and contributing datasets. Composed of
data collected from the literature or provided by community researchers, the
ColabFit Exchange currently (September 2023) consists of 139 datasets spanning
nearly 70,000 unique chemistries, and is intended to continuously grow. In
addition to outlining the software framework used for constructing and
accessing the ColabFit Exchange, we also provide analyses of the data,
quantifying the diversity of the database and proposing metrics for assessing
the relative diversity of multiple datasets. Finally, we demonstrate an
end-to-end IP development pipeline, utilizing datasets from the ColabFit
Exchange, fitting tools from the KLIFF software package, and validation tests
provided by the OpenKIM framework.
