Volume 3 Supplement 1

4th German Conference on Chemoinformatics: 22. CIC-Workshop

Open Access

The perfect fit? Balancing predictive power and computational complexity for an atomistic model as prerequisite for nano-scale simulations

  • A Maaß1,
  • TJ Müller2,
  • L Nikitina1 and
  • M Hülsmann1
Chemistry Central Journal20093(Suppl 1):O14

https://doi.org/10.1186/1752-153X-3-S1-O14

Published: 05 June 2009

When aiming at quantitative predictions for materials that require huge system sizes in simulation – such as polymers – models at coarse-grained level are the natural choice. However, capturing the chemical identity of beads that are void of any individual structure resembling the original compound is a critical point for achieving meaningful predictions. As the coarse grained model inherits the features from an atomistic precursor, the latter needs to be most predictive. This may be achieved by calibrating the detailed model carefully to experimental data, thereby enhancing the atomistic model structure with most realistic behaviour [1].

Diverse strategies like e.g. simplex optimization [2][3], interactive design parameter optimization [4][5], i.e. local optimization versus global search, have been applied to study the polymer precursor ethylene-epoxide and have been intensively investigated in order to identify a viable route to a perfectly tailored atomistic model. Obviously, each strategy has its profits and limitations, the bottom-line being that a final model needs to yield results that are not only accurate, but also to be robust with respect to transfer between independent program packages. For ethylene-oxide several competing models have been published [1, 6][7][8][9][10], however not all are suited for a later coarse graining step. The present field report details newly created models, as well as the tested methods, thus it documents the progress with respect to the long term objective of accurate property predictions for nano-scale simulations.

Authors’ Affiliations

(1)
Fraunhofer Institute SCAI, Schloss Birlinghoven
(2)

References

  1. Müller TJ, Roy S, Zhao W, Maaß A, Reith D: Fluid Phase Equilib. 2008.Google Scholar
  2. Faller R, Schmitz H, Biermann O, Müller-Plathe F: J Comp Chem. 1999, 20 (10): 1009-10.1002/(SICI)1096-987X(19990730)20:10<1009::AID-JCC3>3.0.CO;2-C.View ArticleGoogle Scholar
  3. Müller-Plathe F: Comput Phys Commun. 1993, 78: 77-10.1016/0010-4655(93)90144-2.View ArticleGoogle Scholar
  4. Thole CA, Nikitina L, Nikitin I, Steffes-Iai D, Roel K, Bruns J: Fraunhofer Publica. 2007Google Scholar
  5. Steffes-IAi D, Thole CA, Nikitin I, Nikitina L: ERCIM News. 2008, 73: 29-Google Scholar
  6. Wielopolski PA, Smith ER: Mol Physics. 1985, 54 (2): 467-10.1080/00268978500100371.View ArticleGoogle Scholar
  7. Mountain RD: J Phys Chem B. 2005, 109: 13352-10.1021/jp051379k.View ArticleGoogle Scholar
  8. Li X, Zhao L, Cheng T, Liu L, Sun H: Fluid Phase Equilib. 2008.Google Scholar
  9. Olson JD, Wilson LC: Fluid Phase Equilibria. 2008.Google Scholar
  10. Eckl B, Vrabec J, Hasse H: Fluid Phase Equilibria. 2008.Google Scholar

Copyright

© Maaß et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.