Solubility curves and nucleation rates from molecular dynamics for polymorph prediction - moving beyond lattice energy minimization.
Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by embarrassingly parallel, multiple replica, large-scale molecular dynamics simulations, we report on a new method concerned with predicting crystal structures using the kinetics and solubility of the low energy polymorphs predicted by lattice energy minimization. The proposed molecular dynamics simulation methodology provides several new predictions to the field of crystallization. (1) The methodology is shown to correctly predict the kinetic preference for β-glycine nucleation in water relative to α- and γ-glycine. (2) Analysis of nanocrystal melting temperatures show γ- nanocrystals have melting temperatures up to 20 K lower than either α- or β-glycine. This provides a striking explanation of how an energetically unstable classical nucleation theory (CNT) transition state complex leads to kinetic inaccessibility of γ-glycine in water, despite being the thermodynamically preferred polymorph predicted by lattice energy minimization. (3) The methodology also predicts polymorph-specific solubility curves, where the α-glycine solubility curve is reproduced to within 19% error, over a 45 K temperature range, using nothing but atomistic-level information provided from nucleation simulations. (4) Finally, the methodology produces the correct solubility ranking of β- > α-glycine. In this work, we demonstrate how the methodology supplements lattice energy minimization with molecular dynamics nucleation simulations to give the correct polymorph prediction, at different length scales, when lattice energy minimization alone would incorrectly predict the formation of γ-glycine in water from the ranking of lattice energies. Thus, lattice energy minimization optimization algorithms are supplemented with the necessary solvent/solute dependent solubility and nucleation kinetics of polymorphs to predict which structure will come out of solution, and not merely which structure has the most stable lattice energy.
School of Chemical Engineering, Purdue University, 480 West Stadium Mall, West Lafayette, Indiana 47907, USA. ramkrish@ecn.purdue.edu.
Conor
http://orcid.org/0000-0001-8158-5116
C
Parks
School of Chemical Engineering, Purdue University, 480 West Stadium Mall, West Lafayette, Indiana 47907, USA. ramkrish@ecn.purdue.edu.
Andy
A
Koswara
School of Chemical Engineering, Purdue University, 480 West Stadium Mall, West Lafayette, Indiana 47907, USA. ramkrish@ecn.purdue.edu.
Frank
F
DeVilbiss
Process Research & Development, AbbVie, Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, USA.
Hsien-Hsin
HH
Tung
Process Research & Development, AbbVie, Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, USA.
Nandkishor K
NK
Nere
Process Research & Development, AbbVie, Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, USA.
Shailendra
S
Bordawekar
School of Chemical Engineering, Purdue University, 480 West Stadium Mall, West Lafayette, Indiana 47907, USA. ramkrish@ecn.purdue.edu.
Zoltan K
ZK
Nagy
School of Chemical Engineering, Purdue University, 480 West Stadium Mall, West Lafayette, Indiana 47907, USA. ramkrish@ecn.purdue.edu.
Doraiswami
D
Ramkrishna