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This content will become publicly available on January 3, 2019

Title: A program for automatically predicting supramolecular aggregates and its application to urea and porphin [A programme for the automated geometry prediction of supra-molecular aggregates and its application to the examples of urea and porphin]

Not only the molecular structure but also the presence or absence of aggregates determines many properties of organic materials. Theoretical investigation of such aggregates requires the prediction of a suitable set of diverse structures. Here, we present the open–source program EnergyScan for the unbiased prediction of geometrically diverse sets of small aggregates. Its bottom–up approach is complementary to existing ones by performing a detailed scan of an aggregate's potential energy surface, from which diverse local energy minima are selected. We crossvalidate this approach by predicting both literature–known and heretofore unreported geometries of the urea dimer. We also predict a diverse set of dimers of the less intensely studied case of porphin, which we investigate further using quantum chemistry. For several dimers, we find strong deviations from a reference absorption spectrum, which we explain using computed transition densities. Furthermore, this proof of principle clearly shows that EnergyScan successfully predicts aggregates exhibiting large structural and spectral diversity.
ORCiD logo [1] ;  [2] ; ORCiD logo [3] ;  [4]
  1. Friedrich Schiller Univ., Jena (Germany); Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany)
  2. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  3. Friedrich Schiller Univ., Jena (Germany); Center for Energy and Environmental Chemistry Jena, Jena (Germany)
  4. Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany); SciClus GmbH & Co. KG, Moritz-von-Rohr-StraBe, Jena (Germany)
Publication Date:
Grant/Contract Number:
AC02-76SF00515; FKZ 03EK3507
Accepted Manuscript
Journal Name:
Journal of Computational Chemistry
Additional Journal Information:
Journal Volume: 39; Journal Issue: 13; Journal ID: ISSN 0192-8651
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; software; porphin; urea; aggregation; geometry prediction
OSTI Identifier: