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Prediction and Calculation of Crystal Structures pp 25–58Cite as

General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules

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Part of the book series: Topics in Current Chemistry ((TOPCURRCHEM,volume 345))

Abstract

The prediction of the possible crystal structure(s) of organic molecules is an important activity for the pharmaceutical and agrochemical industries, among others, due to the prevalence of crystalline products. This chapter considers the general requirements that crystal structure prediction (CSP) methodologies need to fulfil in order to be able to achieve reliable predictions over a wide range of organic systems. It also reviews the current status of a multistage CSP methodology that has recently proved successful for a number of systems of practical interest. Emphasis is placed on recent developments that allow a reconciliation of conflicting needs for, on the one hand, accurate evaluation of the energy of a proposed crystal structure and on the other hand, comprehensive search of the energy landscape for the reliable identification of all low-energy minima. Finally, based on the experience gained from this work, current limitations and opportunities for further research in this area are identified. We also consider issues relating to the use of empirical models derived from experimental data in conjunction with ab initio CSP.

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Notes

  1. 1.

    See Sect. 3.5.2 for details of the current implementation.

  2. 2.

    In fact, the algorithms also recognise a third class of CDFs which can be fixed at user-provided values (e.g. in order to exploit a priori available experimental information in performing more targeted searches). However, in the interests of clarity of presentation, we omit this complication from the mathematical descriptions provided in this chapter.

  3. 3.

    The actual implementations also include the +pV term in the objective function which, therefore, corresponds to lattice enthalpy. However, in the interests of simplicity of presentation, this is omitted here and in subsequent discussion.

  4. 4.

    As already mentioned, these LAMs can be stored in persistent LAM databases to be re-used in later calculations, such as those required for the subsequent refinement stage.

  5. 5.

    Including the +pV term.

  6. 6.

    In the case of our CSP methodology, this is the computed value of the lattice enthalpy (as opposed to the true lattice enthalpy) of the crystal structure.

  7. 7.

    Either as part of the energy minimisation calculations or in the form of an a posteriori adjustment of the type discussed in Sect. 4.3.

Abbreviations

API:

Active pharmaceutical ingredient

CCDC:

Cambridge Crystallographic Data Centre

CDF:

Conformational degree of freedom

CSD:

Cambridge Structural Database

CSP:

Crystal structure prediction

DFT:

Density functional theory

DFT+D:

Dispersion corrected density functional theory

LAM:

Local approximate model

QM:

Quantum mechanical

rmsd15 :

Root mean square deviation of the 15-molecule coordination sphere

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Acknowledgements

We wish to acknowledge the major contributions made by P.G. Karamertzanis, M. Vasileiadis and M. Habgood to the fundamentals and implementation of CrystalPredictor and CrystalOptimizer. We are grateful to Professor S.L. Price for many useful discussions and collaboration, and for supplying the DMACRYS code for use within CrystalOptimizer. Financial support for the work reported here was provided by the United Kingdom’s Engineering & Physical Sciences Research Council (EPSRC) under grants EP/E016340, EP/J003840/1 and EP/J014958/1.

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Authors and Affiliations

  1. Department of Chemical Engineering Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, UK

    Constantinos C. Pantelides, Claire S. Adjiman & Andrei V. Kazantsev

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  1. Constantinos C. Pantelides
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  2. Claire S. Adjiman
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  3. Andrei V. Kazantsev
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Correspondence to Constantinos C. Pantelides .

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Editors and Affiliations

  1. Dept. of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Sule Atahan-Evrenk

  2. Dept. of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Alan Aspuru-Guzik

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Pantelides, C.C., Adjiman, C.S., Kazantsev, A.V. (2014). General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules. In: Atahan-Evrenk, S., Aspuru-Guzik, A. (eds) Prediction and Calculation of Crystal Structures. Topics in Current Chemistry, vol 345. Springer, Cham. https://doi.org/10.1007/128_2013_497

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