470151 A Mechanistic Growth Model for Organic Salt Morphology Prediction

Thursday, November 17, 2016: 1:15 PM
Cyril Magnin III (Parc 55 San Francisco)
Jinjin Li, Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA and Michael F. Doherty, Chemical Engineering, UC Santa Barbara, Santa Barbara, CA

There is growing interest in the pharmaceutical sector to develop strategies for digital drug product design, starting with the molecular structure of a target molecule and creating good initial estimates for all the solid form characteristics, including crystal morphology. For morphology prediction, a detailed understanding of crystal growth is vital. In the past several decades various growth models have been proposed to predict the crystal morphology of organic molecules, which laid the foundation to design schemes to manipulate and tailor the crystal morphology in accordance with the application requirements. Recent developments include [1-2].

However, the prediction of growth rates for organic salts still remains blank, due to their complicated crystal structures and complex atomic interactions.  For example, organic salts usually consist of nonstoichiometric ions as the growth units, which makes it difficult to find the periodic bond chains (PBCs) within the crystal lattice; moreover, the short-range intermolecular interactions, and the long-range electrostatic interactions in organic salts may need to be considered; the kink sites in organic salts are complicated due to the existence of positive and negative ions, etc. With these considerations in mind, we propose a mechanistic growth model for organic salt morphology prediction, which identifies periodic bond chains (PBCs) using the concept of building units, and accurately captures the short-range/long-range electrostatic interactions and partial charges. We illustrate the method on ammonium acetate grown from the vapor phase. The electrostatic interaction energies in the kink sites were calculated using a space partitioning method that is computationally efficient. This algorithm for studying the solid-state interactions and building units is based on a mechanistic spiral growth model, which may be useful for predicting the morphology of a wide variety of organic salts.


  1. Li J, Tilbury CJ, Kim SH, Doherty MF, A design aid for crystal growth engineering. Progress in Materials Science 82, 1-38 (2016).

  2. Li J, Tilbury CJ, Joswiak MN, Peters B, Doherty MF, Rate expressions for kink attachment and detachment during crystal growth. Crystal Growth & Design DOI:10.1021/acs.cgd.6b00292 (2016).

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