A hierarchy of composite quantum chemical models for applications in materials chemistry and nanoscience

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Krishnan Raghavachari from Indiana University is supported by an award from the Chemical Theory, Models and Computational Methods program to develop a set of hierarchical quantum chemical computational methods for state-of-the-art applications in materials chemistry and nanoscience. The hierarchical concept involves the use of highly accurate methods on simple systems to validate more approximate and cost-effective methods for complex systems. Such computational methods play a key role in understanding and optimizing the properties of molecules for a range of scientific and technological applications. However, most such applications thus far have been carried out only on relatively small molecules due to the high computational cost associated with accurate methods, and significant challenges remain for the accurate treatment of complex systems that are needed in many applications. The new methods that are being proposed will fill a critical need to treat medium-sized and large molecules accurately, providing systematic well-tested models to the study of materials and nanoscale systems. Computational nanoscience, as a rapidly expanding field, will attract a great deal of student interest, and these projects will provide an excellent opportunity for training the next generation of researchers. Students at all levels will be trained in advanced computational techniques in this work, including undergraduates, graduate students, and postdoctoral researchers. In order to accomplish the proposed goals, Raghavachari and coworkers are building on two different research frontiers that have been previously developed using prior NSF funding. In the first part, the CBH (connectivity-based hierarchy), an accurate thermochemical method for larger molecules, is being adapted to provide systematic error corrections in DFT (density functional theory) to yield accuracy comparable to the state-of-the-art coupled cluster calculations, and applied to calculate reliable bond energies relevant for large biofuel molecules. In the second part, their fragment-based composite model (MIM, Molecules-in-Molecules) is being developed to investigate reactive potential energy surfaces, and to optimize strategies to make the method applicable for calculating higher order spectroscopic properties such as nuclear magnetic resonance chemical shift or Raman optical activity on large molecules containing thousands of atoms. Each development is initially being carried out independently and then being merged together for forefront applications. The combined methods are providing unprecedented accuracy for the treatment of complex problems involving materials chemistry and nanoscience. The new computational tools are being developed in a platform-independent manner and can work with multiple quantum chemical packages, and are being made available for widespread use by other research groups. The award also supports work at a summer program at Indiana University with undergraduate students from historically under-served populations.