RemoteSTREAMM: A Web Application for Running HPC Materials Simulations in the Cloud

Computation plays an important role in materials science, engineering, nanotechnology, pharmaceutical research and many other research fields. Faster time to market, increased return on investment, and enabling new products are common reasons computation is used in product development. However, surveys have shown that moderate sized companies are only slowly adopting materials simulations and taking advantage of the potential increases in innovation and productivity. Statement of how this problem is being addressed Improving the adoption of computational chemistry software is addressed by developing a frame- work to help streamline complex simulation workflows (STREAMM) involving molecular dynamics and quantum chemistry simulations. This framework will be provided in a user-friendly application capable of taking advantage of cloud computing resources, such as Amazon’s Web Services (AWS) or Microsoft’s Azure platform, for the pharmaceutical and materials industries. What was done in Phase I Phase I developed and tested Docker containers enabling molecular dynamics simulations (LAMMPS) and density functional theory calculations (NWChem) to be run on AWS clusters. Excellent parallel scaling efficiency was demonstrated for both codes on multi-node clusters. Containers were also developed for the STREAMM framework and another quantum chemistry code (QMCPack). Initial products are planned for sale in the UberCloud marketplace in the near term. What is planned for Phase II Phase II work will include additional simulation codes to be containerized and made available through the UberCloud marketplace (available for both AWS and Microsoft Azure), such as SP- PARKS (kinetic Monte Carlo) and AWSEM-MD (appropriate for ligand binding in drug discovery) that are more appropriate for our targeted markets. Also, specific industry workflows, defined by our identified beta testers who have provided support letters, will be implemented in a new application ChemiStream. Lastly, open-source machine learning and artificial intelligence methods will be provided to leverage recent successes in drug discovery.Commercial applications and other benefits We are seeking to improve the drug discovery process in the pharmaceutical industry, and the product development cycle in materials processing companies. By making cutting-edge, open- source software from the national labs easier to use, their entire user communities, including those in academia and at the national labs, will benefit as well.