Porting Scalable Computational Chemistry to Cloud Computing

The Science

Cloud computing provides convenient access to resources such as networks, servers, storage, applications, and services. A research team converted several sets of computational chemistry software and libraries to a cloud-compatible format. Using the example of per- and poly-fluoroalkyl substances (PFAS, or “forever” chemicals), the team demonstrated that the cloud-based simulations were both accurate and efficient at modeling PFAS degradation. The new workflows were effective for static and time-dependent processes. Continuing research will broaden the types of test case chemical problems solved through the cloud computing workflows.

The Impact

Cloud computing is emerging as a powerful tool to enable broader, democratized access to advanced computational resources. It also can help manage the demand for high-performance computing resources, take full advantage of the parallel computational chemistry software developed by the Department of Energy (DOE) over the last decade, and enable complex workflows for performing chemistry simulations with a controllable level of accuracy. This would then allow the most advanced computational resources, such as leadership computing facilities, to be reserved for the largest and most complex simulations. Cloud computing will also facilitate the development of new predictive machine learning models trained using the high-accuracy data generated by current electronic structure methods.

Summary

Cloud computing is a distributed computing model that provides network access to a shared pool of configurable computing resources. It enables rapid provisioning and release of these resources with minimal management effort or service provider interaction. Cloud computing is becoming increasingly powerful due to advances in network technology and the emerging capability to apply high-performance computing architectures in the cloud. It also plays a vital role in democratizing computing resources and increasing access to more scientists, including those who are not experts in computational chemistry. 

A multi-institutional team demonstrated that cloud computing, through Azure Quantum Elements, is a powerful tool for hosting a wide range of electronic structure parallel implementations for chemical processes and transformations across spatial and temporal scales. They conducted analysis using legacy and recent software developed using efficient parallel tensor libraries. This infrastructure enabled performance verification for methodologies based on various representations of quantum dynamics, including wave function, electron density, and Green’s function approaches. The team also highlighted the significance of cloud computing to the emerging hybrid quantum/classical computing infrastructure. An example case study of PFAS demonstrated the role of specialized computational chemistry workflows in understanding their degradation mechanisms. These workflows can use the joint power of cloud computing and leadership computational facilities to perform accurate chemical simulations.

Contact

Karol Kowalski, Pacific Northwest National Laboratory, karol.kowalski@pnnl.gov  

Funding

This material was based upon work supported by the “Transferring exascale computational chemistry to cloud computing environment and emerging hardware technologies (TEC4)” project, which is funded by the Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences program, the Division of Chemical Sciences, Geosciences, and Biosciences (under Grant No. FWP 82037). This work was also supported by the DOE, Office of Science, Basic Energy Sciences program, Division of Chemical Sciences, Geosciences, and Biosciences at the Pacific Northwest National Laboratory (PNNL) under Grant No. FWP 70942 (D.M.-R., E.A., N.G., J.J.R., F.D.V., K.K., N.P.B., A.P., K.M.H., B.P., and S.S.X.) at the Center for Scalable Predictive methods for Excitations and Correlated phenomena (SPEC) under Grant No. FWP 79715 (D.M.-R., A.P., N.P.B., H.P., E.M., K.K., and N.G.) at the Center for Many-Body Methods, Spectroscopies, and Dynamics for Molecular Polaritonic Systems (MAPOL), which are funded as part of the Computational Chemical Sciences (CCS), and under Grant No. FWP 72689 in the Center for Embedding QC into Many-body Frameworks for Strongly Correlated Molecular and Materials Systems (B.P., N.P.B., E.J.B., and K.K.), funded by the Department of Energy, Office of Science, Basic Energy Sciences program, Division of Chemical Sciences, Geosciences and Biosciences at PNNL. K.K. and N.P.B. acknowledge support from Quantum Science Center (QSC), a National Quantum Information Science Research Center of the Department of Energy (under FWP 76213). N.P.B. also acknowledges support from the Laboratory Directed Research and Development (LDRD) Program at PNNL. E.J.B. also acknowledges support from the U.S. Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) under Grant Nos. ER-1735, ER-2725, and ER19-1239. A.B. acknowledges the resources of the Argonne Leadership Computing Facility, a Department of Energy Office of Science user facility at Argonne National Laboratory and is based on research supported by the DOE Office of Science-Advanced Scientific Computing Research Program, under Contract No. DE-AC02-06CH11357. The development of NWChem, NWChemEx, and Arrows also acknowledges support from the Department of Energy, Basic Energy Sciences program, Chemical Sciences, Geosciences, and Biosciences Division (Sandia-Livermore CCS, PNNL quantum information science (QIS), and PNNL Geosciences projects), and Biological and Environmental Research program EMSL operations for supporting the development of NWChem, NWChemEx, and Arrows (supported by SERDP). PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the DOE under DOE Contract No. DE-AC05-76RL1830. The development of NWChem, NWChemEx, and Arrows also used resources of the National Energy Research Scientific Computing Center (NERSC), a user facility supported by the Office of Science of the DOE under Contract No. DE-AC02-05CH11231. This report has not been subject to review by SERDP and, therefore, does not necessarily reflect their views and no official endorsement should be inferred.