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Toward Automated Tools for Characterization of Molecular Porosity.

Ismael Gómez GarcíaMarco BernabeiMaciej Haranczyk
Published in: Journal of chemical theory and computation (2018)
The emerging advanced porous materials, e.g. extended framework materials and porous molecular materials, offer an unprecedented level of control of their structure and function. The enormous possibilities for tuning these materials by changing their building blocks mean that, in principle, optimally performing materials for a variety of applications can be systematically designed. However, the process of finding a set of optimal structures for a given application requires computational high-throughput tools to analyze and sieve through many candidate materials. In particular, in the case of porous molecular materials, the analysis and selection of a molecule is one of the key aspects as the structure of the molecule determines the structure of the resulting material, and very often the porosity of the molecule significantly contributes to the porous properties of the resulting material. In this work, we introduce definitions and algorithms to characterize porosity at the molecular level, along with a software implementation of these algorithms. We demonstrate applications of the software tool in the discovery and characterization of porous molecules among ca. 94 million molecules currently enlisted in the PubChem database.
Keyphrases
  • high throughput
  • machine learning
  • metal organic framework
  • deep learning
  • healthcare
  • primary care
  • highly efficient
  • emergency department
  • single molecule