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Connor Barlow

Publications and source records attributed to Connor Barlow.

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Predicting THz Generation Capability of Organic Crystals through Data Mining and Crystal Nonlinearity Models

We report the use of DFT computation and mathematical models to predict the nonlinear dielectric polarization (P^{NL}) and nonlinear susceptibility coefficients (\chi^{(2)}_{IJK}) of organic materials based on their crystal structures. We apply this computation approach on single-component crystals, co-crystals and ionic crystals found through data mining the Cambridge Structural Database. We verify these computational results with experimental terahertz (THz) generation efficiencies for known THz generators, demonstrating consistency between the measurements and the computed values. Several mined structures show similar or larger PNL values compared to state-of-the-art THz generation crystals DAST, OH1 and BNA, suggesting great potential for their use in nonlinear optical (NLO) applications. Importantly, we also compared the resulting model of nonlinear optical tensor components with commonly-used simplifications of nonlinearity, showing that the comprehensive approach should be the standard method to evaluate the nonlinear optical properties of single-crystalline materials.

cond-mat.mtrl-sci

Data Mining for Terahertz Generation Crystals

We demonstrate a data mining approach to discover and develop new organic nonlinear optical crystals that produce intense pulses of terahertz radiation. We mine the Cambridge Structural Database for non-centrosymmetric materials and use this structural data in tandem with density functional theory calculations to predict new materials that efficiently generate terahertz radiation. This enables us to (in a relatively short time) discover, synthesize, and grow large, high-quality crystals of four promising materials and characterize them for intense terahertz generation. In a direct comparison to the current state-of-the-art organic terahertz generation crystals, these new materials excel. The discovery and characterization of these novel terahertz generators validates the approach of combining data mining with density functional theory calculations to predict properties of high-performance organic materials, potentially for a host of exciting applications.

cond-mat.mtrl-sci