Structure Prediction and Design of Molecular Crystals with the GAtor Genetic Algorithm

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NONTECHNICAL SUMMARYThis award supports research and educational activities aimed at developing and applying computational methods to predict crystal structures of molecular crystals. Molecular crystals are solids comprised of molecular building blocks. They are used extensively in a variety of applications that require packing molecular ingredients into structures with uniform, reproducible properties, including pharmaceuticals, energetic materials, and organic electronics. The properties and performance of molecular solids are inextricably linked to their crystal structure. Because molecular crystals are held together by relatively weak inter-molecular interactions, as opposed to the strong chemical bonds that hold atoms together to form molecules, the same molecule may crystallize in several different crystal structures, known as polymorphs. Polymorphs of the same molecule may have markedly different physical and chemical properties. The ability to predict all the possible polymorphs of a given molecule and their properties by computer simulations is of paramount importance across industries whose products are marketed in the form of molecular crystals. In this research project, the PI and her team will develop algorithms for prediction of molecular crystal structures and for design of crystal structures with improved properties. This award also supports the PI's educational and outreach activities, which include training of graduate and undergraduate students in high performance computing and machine learning, new curriculum development, and organization of "Women Leaders in Science and Engineering" luncheon meetings where female students meet with women seminar speakers, providing them with mentorship and networking opportunities to help retain them in science and engineering and advance their careers. TECHNICAL SUMMARYThis award supports research and educational activities aimed at developing and applying computational methods to predict crystal structures of molecular crystals. Molecular crystals are bound by weak dispersion interactions that generate potential energy landscapes with many local minima that may be extremely close in energy. This gives rise to polymorphism, the crystallization of the same molecule in different structures. Crystal structure may profoundly influence the physical and chemical properties, and hence the functionality of molecular solids in diverse applications, including pharmaceuticals, energetic materials, and organic electronics. Therefore, the ability to predict the structure and properties of molecular crystals is of paramount importance. In this research project, the PI and her team will develop algorithms for prediction of molecular crystal structures and for inverse design of crystal structures with improved properties. Specifically, multi-component crystals and crystals of flexible molecules will be targeted. This will be achieved through the development of new methods for configuration space exploration, combining first-principles simulations, optimization algorithms, and machine learning in seamlessly integrated workflows. These will be implemented in open source codes, parallelized and designed for efficient execution on high-performance computers. Crystal structure prediction datasets will be made publicly available as a resource for other researchers. This award also supports the PI's educational and outreach activities, which include training of graduate and undergraduate students in high performance computing and machine learning, new curriculum development, and organization of "Women Leaders in Science and Engineering" luncheon meetings where female students meet with women seminar speakers, providing them with mentorship and networking opportunities to help retain them in science and engineering and advance their careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.