Design and Implementation of Scientific Software Components to Enable Multiscale Modeling: The Effective Fragment Potential (QM/EFP) Method

The design and development of scientific software components to provide an interface to the effective fragment potential (EFP) methods are reported. Multiscale modeling of physical and chemical phenomena demands the merging of software packages developed by research groups in significantly different fields. Componentization offers an efficient way to realize new high performance scientific methods by combining the best models available in different software packages without a need for package readaptation after the initial componentization is complete. The EFP method is an efficient electronic structure theory based model potential that is suitable for predictive modeling of intermolecular interactions in large molecular systems, such as liquids, proteins, atmospheric aerosols, and nanoparticles, with an accuracy that is comparable to that of correlated ab initio methods. The developed components make the EFP functionality accessible for any scientific component-aware software package. The performance of the component is demonstrated on a protein interaction model, and its accuracy is compared with results obtained with coupled cluster methods

Computational Thermochemistry and Benchmarking of Reliable Methods

During the first and second years of the Computational Thermochemistry and Benchmarking of Reliable Methods project, we completed several studies using the parallel computing capabilities of the NWChem software and Molecular Science Computing Facility (MSCF), including large-scale density functional theory (DFT), second-order Moeller-Plesset (MP2) perturbation theory, and CCSD(T) calculations. During the third year, we continued to pursue the computational thermodynamic and benchmarking studies outlined in our proposal. With the issues affecting the robustness of the coupled cluster part of NWChem resolved, we pursued studies of the heats-of-formation of compounds containing 5 to 7 first- and/or second-row elements and approximately 10 to 14 hydrogens. The size of these systems, when combined with the large basis sets (cc-pVQZ and aug-cc-pVQZ) that are necessary for extrapolating to the complete basis set limit, creates a formidable computational challenge, for which NWChem on NWMPP1 is well suited

A Component Architecture for High-Performance Scientific Computing

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry