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Open material databases storing hundreds of thousands of material structuresand their corresponding properties have become the cornerstone of moderncomputational materials science. Yet, the raw outputs of the simulations, suchas the trajectories from molecular dynamics simulations and charge densitiesfrom density functional theory calculations, are generally not shared due totheir huge size. In this work, we describe a cloud-based platform to facilitatethe sharing of raw data and enable the fast post-processing in the cloud toextract new properties defined by the user. As an initial demonstration, ourdatabase currently includes 6286 molecular dynamics trajectories for amorphouspolymer electrolytes and 5.7 terabytes of data. We create a public analysislibrary at https://github.com/TRI-AMDD/htp_md to extract multiple propertiesfrom the raw data, using both expert designed functions and machine learningmodels. The analysis is run automatically with computation in the cloud, andresults then populate a database that can be accessed publicly. Our platformencourages users to contribute both new trajectory data and analysis functionsvia public interfaces. Newly analyzed properties will be incorporated into thedatabase. Finally, we create a front-end user interface athttps://www.htpmd.matr.io for browsing and visualization of our data. Weenvision the platform to be a new way of sharing raw data and new insights forthe computational materials science community.

A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations