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]

Sachse, Torsten; Martinez, Todd J.; Dietzek, Benjamin; Presselt, Martin

doi:10.1002/jcc.25151

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]

Journal Article · Wed Jan 03 00:00:00 EST 2018 · Journal of Computational Chemistry

DOI:https://doi.org/10.1002/jcc.25151· OSTI ID:1457419

^[1]; Martinez, Todd J. ^[2];

^[3]; Presselt, Martin ^[4]

Friedrich Schiller Univ., Jena (Germany); Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany)
Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
Friedrich Schiller Univ., Jena (Germany); Center for Energy and Environmental Chemistry Jena, Jena (Germany)
Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany); SciClus GmbH & Co. KG, Moritz-von-Rohr-StraBe, Jena (Germany)

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.

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Research Organization:: SLAC National Accelerator Lab., Menlo Park, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-76SF00515; FKZ 03EK3507

OSTI ID:: 1457419

Journal Information:: Journal of Computational Chemistry, Vol. 39, Issue 13; ISSN 0192-8651

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 9 works

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97 MATHEMATICS AND COMPUTING
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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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]

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