Heterogeneous catalysis and the design of new catalysts is a grand challenge problem in computational chemistry that will require the capabilities of exascale computing. The GAMESS project is extending methods and algorithms based on chemical fragmentation methods and coupling these with high-fidelity Quantum Chemistry (QC) and Quantum Monte Carlo (QMC) simulations to solve this problem. Through computation on a well-defined representative heterogeneous catalysis problem comprising mesoporous silica nanoparticles, GAMESS will demonstrate the capability to model physical systems requiring chemical interactions that involve many thousands of atoms, indicating a new ability to model complex chemical processes.
Computational chemistry is a key research technique that enables scientists to use precise simulations of atomic and molecular interactions to understand the function of novel materials and biomolecules, design new drugs, and create technologies in fields ranging from environmental remediation to medicine. However, these simulations require significant computational resources owing to the complex chemical and quantum mechanical processes involved in chemical bonding, and even modern supercomputers can struggle with models of large atomic systems such as heterogenous catalysts and complex biomolecules.
The Exascale Computing Project’s GAMESS application was created to address this shortcoming. The application significantly expands capabilities in computational chemistry, allowing for simulations of far larger and more complex systems in shorter timescales. Researchers can use GAMESS to design and test aptamers—DNA and RNA fragments—for detecting novel pathogens, explore how various materials can be used to remove CO2 from the atmosphere and transform it into useful compounds, and design more effective catalysts for chemical manufacturing processes.
The extreme complexity of atomic and molecular interactions has limited the scope of legacy computational chemistry simulations. Traditional systems can typically model interactions between hundreds of atoms before being forced to sacrifice accuracy to reduce simulation durations to feasible levels. However, simulations of this size prohibit the direct study of critical systems, including biomolecular interactions and most catalytic reactions. To compensate for this deficit, researchers have resorted to using smaller models that approximate complete systems. Unfortunately, these approximations are often inaccurate and result in incomplete or erroneous results when compared with information gathered from real-world experiments.
The GAMESS team has addressed this shortcoming by delivering an application that leverages exascale computing to extend the size of physically accurate computational chemistry simulations by orders of magnitude. Using GAMESS, researchers can now model systems of up to tens of thousands of atoms without sacrificing accuracy, which allows for the direct study of a vast number of molecular systems that were not previously accessible. The application team demonstrated this capability by modeling the dynamics and reaction mechanism of mesoporous silica nanoparticles—a ubiquitous and highly effective catalyst used in chemical synthesis—using tens of thousands of atoms.
As a key research tool, GAMESS software is already used by more than 150,000 researchers worldwide. The advances that this application brings to computational chemistry will significantly accelerate technological development in active areas of research such as climate mitigation, materials design, and drug synthesis. These developments will pave the way for new carbon sequestration technologies to reduce the impact of the greenhouse effect, methods for generating useful compounds such as ethanol, and more effective medications and more sensitive screens for pathogenic illnesses. As computational chemistry continues to advance our fundamental understanding of chemical interactions, GAMESS ensures that current and future high-performance computing systems can be fully leveraged for these simulations.
To take full advantage of exascale architectures, it is critical that application software be developed that can exploit multiple layers of parallelism and take advantage of emerging low-power architectures that dramatically lower energy and power costs without significant deterioration of time-to-solution. This work will develop ab initio methods for GAMESS based on fragmentation methods that have been shown to scale beyond the petascale combined with QMC. To attain exascale performance, GAMESS will be refactored to take advantage of modern computer hardware and software, and the capabilities of the C++ libcchem code that is codeveloped with GAMESS will be greatly expanded. Concurrently, performance analyses will be conducted for the broad array of electronic structure methods in GAMESS on current and emerging architectures to assess their ability to decrease time-to-solution. The improved codes that are developed will be brought to bear on the heterogeneous catalysis problem, specifically using mesoporous silica nanoparticles (MSNs), requiring thousands of atoms, as a template.
MSNs are highly effective, selective heterogeneous catalysts for a wide variety of important reactions including the production of carbinolamine, which is a starter material for other structures. MSN selectivity is provided by “gatekeeper” groups that allow only desired reactants A to enter the pore, keeping undesirable species B from entering the pore. The presence of a solvent further complicates the computational problem of simulating the heterogeneous catalysis. Accurate electronic structure calculations are needed to deduce the reaction mechanism(s), including the effects of various solvents, and to subsequently design even more effective catalysts. The narrow pores (3–5 nm) can create a diffusion problem that can prevent product molecules from exiting the pore. Therefore, in addition to elucidating the reaction mechanism, the dynamics of the reaction process should be studied, and a sufficiently realistic cross section of the pore must be included. Small models are commonly used to approximate this type of system, with the expectation that a small model might provide insight into the system it represents. However, a recent computational study of the MSN catalysis of carbinolamine formation demonstrated that small proxy models are inadequate, both qualitatively and quantitatively.
This project involves computing both energetics and dynamics on a model reaction with a representative MSN. An adequate representation of the MSN pore requires thousands of atoms with an appropriate basis set. For example, 5,000 heavy atoms with the aug-cc-pVTZ basis set requires more than 500,000 basis functions, not including the hydrogen atoms, the reacting molecules, and especially the solvent molecules.
The energy surface will be mapped via GAMESS calculations using the EFMO + resolution of identity (RI)-MP2 methodology, with refined calculations using the EFMO+CR-CC coupled cluster approach or GAMESS EFMO + QMC approach for accurate reaction rates. The pore selectivity dynamics will be computed with an MD approach requiring approximately 10,000 energetics-type calculations using the GAMESS+FMO code.