Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction

2017 ◽  
Vol 13 (2) ◽  
pp. 441-450 ◽  
Author(s):  
Sarah R. Whittleton ◽  
A. Otero-de-la-Roza ◽  
Erin R. Johnson
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Dispersion Model ◽  
Crystal Structure Prediction

scholarly journals Composite and Low-Cost Approaches for Molecular Crystal Structure Prediction

2018 ◽  
Vol 14 (4) ◽  
pp. 2265-2276 ◽  
Author(s):  
Luc M. LeBlanc ◽  
Alberto Otero-de-la-Roza ◽  
Erin R. Johnson
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Low Cost ◽  
Crystal Structure Prediction

scholarly journals Reliable and practical computational description of molecular crystal polymorphs

2019 ◽  
Vol 5 (1) ◽  
pp. eaau3338 ◽  
Author(s):  
Johannes Hoja ◽  
Hsin-Yu Ko ◽  
Marcus A. Neumann ◽  
Roberto Car ◽  
Robert A. DiStasio ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Sampling Strategy ◽  
Computational Prediction ◽  
Crystal Structure Prediction ◽  
Free Energies ◽  
Blind Test ◽  
Ranking Strategy ◽  
Excellent Stability

Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs. However, the computational prediction of molecular crystal polymorphs is highly challenging due to the high dimensionality of conformational and crystallographic space accompanied by the need for relative free energies to within 1 kJ/mol per molecule. In this study, we combine the most successful crystal structure sampling strategy with the most successful first-principles energy ranking strategy of the latest blind test of organic crystal structure prediction methods. Specifically, we present a hierarchical energy ranking approach intended for the refinement of relative stabilities in the final stage of a crystal structure prediction procedure. Such a combined approach provides excellent stability rankings for all studied systems and can be applied to molecular crystals of pharmaceutical importance.

scholarly journals Data-efficient machine learning for molecular crystal structure prediction

2021 ◽  
Vol 12 (12) ◽  
pp. 4536-4546
Author(s):  
Simon Wengert ◽  
Gábor Csányi ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Molecular Crystals ◽  
Crystal Structure Prediction ◽  
Learning Models ◽  
Training Scheme ◽  
Efficient Machine ◽  
Machine Learning Models

Using a cluster-based training scheme and a physical baseline, data efficient machine-learning models for crystal structure prediction are developed, enabling accurate structural relaxations of molecular crystals with unprecedented efficiency.

scholarly journals Crystal structure prediction of indomethacin

2014 ◽  
Vol 70 (a1) ◽  
pp. C1540-C1540
Author(s):  
Xiaozhou Li ◽  
Kristoffer Johansson ◽  
Andrew Bond ◽  
Jacco van de Streek
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Density Functional ◽  
Computational Cost ◽  
Stable Form ◽  
Crystal Structure Prediction ◽  
Blind Test

Indomethacin is a non-steroidal anti-inflammatory and antipyretic agent. Because different packing arrangements of the same drug can greatly affect drug properties such as colours, solubility, stability, melting point, dissolution rate and so forth, it is important to predict its polymorphs. The computational prediction of the stable form will reduce undesirable risks in both clinical trials and manufacturing. Reported polymorphs of indomethacin include α, β, γ, δ, ε, η and ζ [1], of which only the thermodynamically stable form γ and the metastable form α are determined. Density functional theory with dispersion-correction (DFT-D) has been used extensively to study molecular crystal structures[2]. It gives better results with a compromise between the computational cost and accuracy towards the reproduction of molecular crystal structures. In the fourth blind test of crystal structure prediction in 2007, the DFT-D method gave a very successful result that predicted all four structures correctly. Rather than using transferable force fields, a dedicated tailor-made force field (TMFF) parameterised by DFT-D calculations[3] is used for every chemical compound. The force field is used to generate a set of crystal structures and delimit a candidate window for energy ranking. The powder diffraction patterns of predicted polymorphs are calculated to compare with experimental data.

scholarly journals GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction

2018 ◽  
Vol 14 (4) ◽  
pp. 2246-2264 ◽  
Author(s):  
Farren Curtis ◽  
Xiayue Li ◽  
Timothy Rose ◽  
Álvaro Vázquez-Mayagoitia ◽  
Saswata Bhattacharya ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Genetic Algorithm ◽  
First Principles ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Crystal Structure Prediction
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