Publications by authors named "James D Green"

Recent years have seen an explosion of interest in organic radicals due to their promise for highly efficient organic light-emitting diodes and molecular qubits. However, accurately and inexpensively computing their electronic structure has been challenging, especially for excited states, due to the spin-contamination problem. Furthermore, while alternacy or "pseudoparity" rules have guided the interpretation and prediction of the excited states of closed-shell hydrocarbons since the 1950s, similar general rules for hydrocarbon radicals have not to our knowledge been found yet.

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The discovery of molecules with tailored optoelectronic properties, such as specific frequency and intensity of absorption or emission, is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the following question: How can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning, and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties.

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Introduction: Design of next-generation ambulance patient compartment requires up-to date anthropometric data of emergency medical service providers (EMSP). Currently, no such data exist in the U.S.

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This report summarizes the deliberations of a multidisciplinary committee, sponsored by the Pharmaceutical Research and Manufacturers of America, on current "best practices" within the U.S. pharmaceutical industry in assessing the role of drug metabolites as potential mediators of the toxicity of new drug products.

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