CAREER: Organic Materials Discovery with the Aid of Digital Crystallography

NONTECHNICAL SUMMARY This CAREER award supports theoretical and computational research and educational activities to advance the understanding of organic materials. Currently, systematic predictive models for developing new organic materials with targeted physical properties are largely missing, due to the fact that molecules can be packed in enormous number of ways in the three dimensional space. This research aims to develop a new computational scheme that allows more efficient discovery of organic materials based on modern crystallography. To achieve this objective, the PI will first digitize the complex crystallographic data into low-dimensional representations that retain the key information about the molecular packing and crystal symmetry. Second, a computational pipeline will be developed to model the organic materials though different levels of theory. Finally, the power of the new infrastructure will be validated by conducting computational screening of organic materials with superior mechanical and ferroelectric properties that can find potential applications in modern technologies. This CAREER award also provides support for education and outreach activities that are centered on promoting open-source software development. Specific objectives include (i) training undergraduate and graduate students in computational physics; (ii) holding the summer/winter camps in scientific computing and Python programming for K-12 students; and (iii) organizing workshops for learning about open-source codes in crystallography and materials modeling. TECHNICAL SUMMARY This CAREER award aims to develop a computational pipeline for digital crystallography that can speed up the discovery of small molecule organic materials for various applications. Nowadays, scientists are seeking to improve the functionalities of organic materials by using chemical and structural manipulations with a design space that can be astronomically large. To develop new organic solids, crystal engineers resort to predictive guidelines. However, systematically discovering new organic crystals from computer simulations remains challenging due to the complex molecular packing as compared to the inorganic counterpart. In this project, the goal is to develop a new approach to design organic materials by performing a data-intensive investigation of complex crystal packing and its impacts on the physical properties. Specifically, the PI will introduce a digitized description for organic crystal data based on modern mathematical theory and implement them into the open-source code PyXtal. A computational pipeline will be developed to allow high-throughput materials modeling at different levels from molecular mechanics, machine learning to quantum mechanics. Based on this infrastructure, computational screenings of organic materials with superior mechanical strength and ferroelectric properties will be conducted. The educational component of this CAREER project will be centered on promoting open-source software development. Specific objectives include (i) training undergraduate and graduate students in computational physics; (ii) holding the summer/winter camps in scientific computing and Python programming for K-12 students; and (iii) organizing workshops for learning about open-source codes in crystallography and materials modeling. 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.