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Creators/Authors contains: "Casetti, Nicholas"

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  1. ; ; ; (, Journal of Chemical Theory and Computation)
    Free, publicly-accessible full text available October 28, 2026
  2. ; ; ; ; (, The Journal of Chemical Physics)
    Free, publicly-accessible full text available November 28, 2025
  3. ; ; ; (, Chemistry – A European Journal)
    Abstract Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high‐throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules;in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field. 
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