Our goal is to develop data-informed models to discover candidate materials to substitute or trap toxic elements. The data challenge is to identify signatures of chemical pathways involving hazardous materials, and explore alternative chemistry processes allowing for substitute candidates.
Models, Data and Tools
We have developed fundamental molecular scale chemistry models that capture both the electronic origins of instability and links to the crystal structure and molecular environment of each species in the compounds. We have used a hybrid approach in computational chemistry modeling to generate data that has been the foundation for our informatics-based analysis. This data is represented through the Hirshfeld Surface. The following tools provide the data and mapping of information for Metal-Organic Framework (MOF) structure. The framework structure can be tuned to create porous materials with well-defined pore geometries, networks and sizes. The tuning of these factors control the storing of toxic elements.
Reference: Xiaozhou Shen, Tianmu Zhang, Scott Broderick, Krishna Rajan, “Correlative Analysis of Metal Organic Framework Structures through Manifold Learning of Hirshfeld Surfaces.” Molecular Systems Design & Engineering, vol. 3, pp. 826-838 (2018)