Materials design via tunable properties
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In the design of novel materials, tunable properties are parameters such as composition or structure that may be adjusted in order to enhance a desired chemical or material property. Trends in tunable properties can be accurately predicted using computational and combinatorial chemistry tools in order to optimize a desired property. I present a study of tunable properties in materials and employ a variety of algorithms that ranges from simple screening to machine learning. In the case of tuning a nanocomposite membrane for olefin/paraffin separations, I demonstrate a rational design approach based on statistical modeling followed by ab initio modeling of the interaction of olefins with various nanoparticles. My simplified model of gases diffusing on a heterogeneous lattice identifies the conditions necessary for optimal selectivity of olefins over paraffins. The ab initio modeling is then applied to identify realistic nanomaterials that will produce such conditions. The second case, [alpha]-Fe₂O₃, commonly known as hematite, is potential solar cell material. I demonstrate the use of a screened search through chemical compound space in order to identify doped hematite-based materials with an ideal band gap for maximum solar absorption. The electronic structure of hematite is poorly treated by standard density functional theory and requires the application of Hartree-Fock exchange in order to reproduce the experimental band gap. Using this approach, several potential solar cell materials are identified based on the behavior of the dopants within the overall hematite structure. The final aspect of this work is a new method for identifying low-energy chemical processes in condensed phase materials. The gap between timescales that are attainable with standard molecular dynamics and the processes that evolve on a human timescale presents a challenge for modeling the behavior of materials. This problem is particularly severe in the case of condensed phase systems where the reaction mechanisms may be highly complicated or completely unknown. I demonstrate the use of support vector machines, a machine-learning technique, to create transition state theory dividing surfaces without a priori information about the reaction coordinate. This method can be applied to modeling the stability of novel materials.