  Open material databases storing hundreds of thousands of material structures
and their corresponding properties have become the cornerstone of modern
computational materials science. Yet, the raw outputs of the simulations, such
as the trajectories from molecular dynamics simulations and charge densities
from density functional theory calculations, are generally not shared due to
their huge size. In this work, we describe a cloud-based platform to facilitate
the sharing of raw data and enable the fast post-processing in the cloud to
extract new properties defined by the user. As an initial demonstration, our
database currently includes 6286 molecular dynamics trajectories for amorphous
polymer electrolytes and 5.7 terabytes of data. We create a public analysis
library at https://github.com/TRI-AMDD/htp_md to extract multiple properties
from the raw data, using both expert designed functions and machine learning
models. The analysis is run automatically with computation in the cloud, and
results then populate a database that can be accessed publicly. Our platform
encourages users to contribute both new trajectory data and analysis functions
via public interfaces. Newly analyzed properties will be incorporated into the
database. Finally, we create a front-end user interface at
https://www.htpmd.matr.io for browsing and visualization of our data. We
envision the platform to be a new way of sharing raw data and new insights for
the computational materials science community.
