scholarly journals Data-driven learning and prediction of inorganic crystal structures

2018 ◽  
Vol 211 ◽  
pp. 45-59 ◽  
Author(s):  
Volker L. Deringer ◽  
Davide M. Proserpio ◽  
Gábor Csányi ◽  
Chris J. Pickard
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Data Driven ◽  
Interatomic Potentials ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Inorganic Crystal

Machine learning-based interatomic potentials, fitting energy landscapes “on the fly”, are emerging and promising tools for crystal structure prediction.

scholarly journals Interpreting computed crystal energy landscapes for pharmaceutical molecules

2014 ◽  
Vol 70 (a1) ◽  
pp. C1615-C1615
Author(s):  
Sarah Price
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Pyridinecarboxylic Acid ◽  
Solid Form ◽  
Pharmaceutical Molecules ◽  
Polymorph Screening

Crystal Structure Prediction (CSP) algorithms aim to generate the thermodynamically feasible crystal structures of a molecule from the chemical diagram, ranking their relative stability by a necessarily approximate estimate of the crystal energy. Such calculations are becoming feasible for molecules of a size and flexibility of small molecule pharmaceuticals. Contrasting the crystal energy landscape, the computer generated structures that are thermodynamically plausible as polymorphs, with the results of experimental polymorph screening, shows that CSP studies are not limited to being a search for the most thermodynamically stable crystal structure but can play a valuable role in understanding polymorphism and the potential complexity of crystallisation behaviour.[1] This presentation will illustrate the use of CSP as a complement to industrial-type solid form screening activities. Examples will include olanzapine, [2] tazofelone, two closely related 5-HT2a agonists and 6-[(5-chloro-2-([(4-chloro-2-fluorophenyl)methyl]oxy)phenyl)methyl]-2-pyridinecarboxylic acid (GSK269984B).[3] This illustrates the use of the crystal energy landscape to understand disorder, help structurally characterise metastable polymorphs and suggest whether there are additional polymorphs to be targeted. Since crystal energy landscapes usually include a wider range of crystal structures than known polymorphs, it raises the scientific question as to what determines which structures can be observed as metastable polymorphs. Thus both scientific as well as technological challenges need to be overcome before we can predict polymorphs.

scholarly journals Crystal structures of two furazidin polymorphs revealed by a joint effort of crystal structure prediction and NMR crystallography

2020 ◽  
Vol 76 (3) ◽  
pp. 322-335 ◽  
Author(s):  
Marta K. Dudek ◽  
Piotr Paluch ◽  
Edyta Pindelska
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Features ◽  
Crystal Structure Determination ◽  
Crystal Structure Prediction ◽  
Crystal Forms ◽  
Space Groups ◽  
Nmr Crystallography

This work presents the crystal structure determination of two elusive polymorphs of furazidin, an antibacterial agent, employing a combination of crystal structure prediction (CSP) calculations and an NMR crystallography approach. Two previously uncharacterized neat crystal forms, one of which has two symmetry-independent molecules (form I), whereas the other one is a Z′ = 1 polymorph (form II), crystallize in P21/c and P 1 space groups, respectively, and both are built by different conformers, displaying different intermolecular interactions. It is demonstrated that the usage of either CSP or NMR crystallography alone is insufficient to successfully elucidate the above-mentioned crystal structures, especially in the case of the Z′ = 2 polymorph. In addition, cases of serendipitous agreement in terms of 1H or 13C NMR data obtained for the CSP-generated crystal structures different from the ones observed in the laboratory (false-positive matches) are analyzed and described. While for the majority of analyzed crystal structures the obtained agreement with the NMR experiment is indicative of some structural features in common with the experimental structure, the mentioned serendipity observed in exceptional cases points to the necessity of caution when using an NMR crystallography approach in crystal structure determination.

scholarly journals Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction

2018 ◽  
Vol 24 (S2) ◽  
pp. 144-145 ◽  
Author(s):  
Yuta Suzuki ◽  
Hideitsu Hino ◽  
Yasuo Takeichi ◽  
Takafumi Hawai ◽  
Masato Kotsugi ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
X Ray

scholarly journals Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

2017 ◽  
Vol 8 (7) ◽  
pp. 4926-4940 ◽  
Author(s):  
Alexander G. Shtukenberg ◽  
Qiang Zhu ◽  
Damien J. Carter ◽  
Leslie Vogt ◽  
Johannes Hoja ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Powder Diffraction ◽  
Structure Prediction ◽  
Prediction Method ◽  
Crystal Structure Prediction ◽  
Free Energies ◽  
Structure Prediction Method

Crystal structures of four new coumarin polymorphs were solved by crystal structure prediction method and their lattice and free energies were calculated by advanced techniques.

scholarly journals CrySPY: a crystal structure prediction tool accelerated by machine learning

2021 ◽  
Vol 1 (1) ◽  
pp. 87-97
Author(s):  
Tomoki Yamashita ◽  
Shinichi Kanehira ◽  
Nobuya Sato ◽  
Hiori Kino ◽  
Kei Terayama ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Prediction Tool ◽  
Crystal Structure Prediction
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