Is zeroth order crystal structure prediction (CSP_0) coming to maturity? What should we aim for in an ideal crystal structure prediction code?

Price, Sarah L.

doi:10.1039/c8fd00121a

Cited by 93 publications

(123 citation statements)

References 81 publications

(60 reference statements)

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“…The variation in Δχ an cryst between the lowest energy unobserved and all the observed polymorphs merely shows that the crystallization of diclofenac might be affected by a strong magnetic field to favor a new form. A more accurate determination of the free energy differences 20 and studies of the crystallization behavior of the known forms would be required to help decide whether it was worth attempting crystallization of diclofenac in a very strong field. However, the development of efficient and reliable computational methods to determine the differences in the χ cryst tensors of pharmaceutical polymorphs is a first necessary step toward the goal of realizing the potential for the polymorphic control of a crystallization process by a magnetic field.…”

Section: Discussionmentioning

confidence: 99%

Calculation of Diamagnetic Susceptibility Tensors of Organic Crystals: From Coronene to Pharmaceutical Polymorphs

et al. 2020

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Understanding why crystallization in strong magnetic fields can lead to new polymorphs requires methods to calculate the diamagnetic response of organic molecular crystals. We develop the calculation of the macroscopic diamagnetic susceptibility tensor, χ cryst , for organic molecular crystals using periodic density functional methods. The crystal magnetic susceptibility tensor, χ cryst , for all experimentally known polymorphs, and its molecular counterpart, χ mol , are calculated for flexible pharmaceuticals such as carbamazepine, flufenamic acid, and chalcones, and rigid molecules, such as benzene, pyridine, acridine, anthracene, and coronene, whose molecular magnetic properties have been traditionally studied. A tensor addition method is developed to approximate the crystal diamagnetic susceptibility tensor, χ cryst , from the molecular one, χ mol , giving good agreement with those calculated directly using the more costly periodic density functional method for χ cryst . The response of pharmaceutical molecules and crystals to magnetic fields, as embodied by χ cryst , is largely determined by the packing in the crystal, as well as the molecular conformation. The anisotropy of χ cryst can vary considerably between polymorphs though the isotropic terms are fairly constant. The implications for developing a computational method for predicting whether crystallization in a magnetic field could produce a novel or different polymorph are discussed.

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Section: Discussionmentioning

confidence: 99%

Calculation of Diamagnetic Susceptibility Tensors of Organic Crystals: From Coronene to Pharmaceutical Polymorphs

et al. 2020

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“…Modern tools of the crystal structure prediction (CSP) produce large datasets of thousands or even millions of simulated crystals based on the same chemical composition. [ 1 ] Many of these crystal structures are geometrically similar, because they were obtained as approximations to local minima of a complicated energy. The available similarity tests miss too many nearly identical structures that are ambiguously represented in different ways.…”

Section: Introduction: Motivations For Similarity Distances From Crysmentioning

confidence: 99%

“…The available similarity tests miss too many nearly identical structures that are ambiguously represented in different ways. That is why Price has summarized the state‐of‐the‐art in CSP as “the embarrassment of over‐prediction.” [ 1 ]…”

Section: Introduction: Motivations For Similarity Distances From Crysmentioning

confidence: 99%

“…A dream CSP solution for the pharma industry would be a reliable method to output a short list of only few most stable polymorphs based on a given molecular input. [ 1 ] The drug design will be substantially sped up if one can enrich any dataset of simulated crystal structures by a justified distance that shows which structures are geometric neighbors, i.e., close to each other according to this distance, and how such neighborhoods are located relatively to each other.…”

Section: Introduction: Motivations For Similarity Distances From Crysmentioning

confidence: 99%

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Voronoi‐Based Similarity Distances between Arbitrary Crystal Lattices

Mosca

Kurlin

2020

Crystal Research and Technology

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This paper develops a new continuous approach to a similarity between periodic lattices of ideal crystals. Quantifying a similarity between crystal structures is needed to substantially speed up the crystal structure prediction, because the prediction of many target properties of crystal structures is computationally slow and is essentially repeated for many nearly identical simulated structures. The proposed distances between arbitrary periodic lattices of crystal structures are invariant under all rigid motions, satisfy the metric axioms and continuity under atomic perturbations. The above properties make these distances ideal tools for clustering and visualizing large datasets of crystal structures. All the conclusions are rigorously proved and justified by experiments on real and simulated crystal structures.

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“…problem of predicting the outcome of assemblies in crystalline materials, and crystal structure prediction is now a mature, but complex, field (see for example, References [11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

Substituent Effects in the Crystal Packing of Derivatives of 4′-Phenyl-2,2′:6′,2″-Terpyridine

et al. 2019

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We report the preparation of a series of new 4′-substituted 2,2′:6′,2″-terpyridines: 4′-(3,5-dimethylphenyl)-2,2′:6′,2″-terpyridine (2), 4′-(3-fluoro-5-methylphenyl)-2,2′:6′,2″-terpyridine (3), 4′-(3,5-difluorophenyl)-2,2′:6′,2″-terpyridine (4), and 4′-(3,5- bis(trifluoromethyl)phenyl)-2,2′:6′,2″-terpyridine (5). The compounds have been characterized by mass spectrometry, solid-state IR spectroscopy and solution NMR and absorption spectroscopies. The single-crystal X-ray diffraction structures of 3, 5 and 6·EtOH (6 = 4′-(3,5-bis(tert-butyl)phenyl)-2,2′:6′,2″-terpyridine) have been elucidated. The molecular structures of the compounds are unexceptional. Since 3 and 5 crystallize without lattice solvent, we are able to understand the influence of introducing substituents in the 4′-phenyl ring and compare the packing in the structures with that of the previously reported 4′-phenyl-2,2′:6′,2″-terpyridine (1). On going from 1 to 3, face-to-face π-stacking of pairs of 3-fluoro-5-methylphenyl rings contributes to a change in packing from a herringbone assembly in 1 with no ring π-stacking to a layer-like packing. The latter arises through a combination of π-stacking of aromatic rings and N…H–C hydrogen bonding. On going from 3 to 5, N…H–C and F…H–C hydrogen-bonding is dominant, supplemented by π-stacking interactions between pairs of pyridine rings. A comparison of the packing of molecules of 6 with that in 1, 3 and 5 is difficult because of the incorporation of solvent in 6·EtOH.

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Is zeroth order crystal structure prediction (CSP_0) coming to maturity? What should we aim for in an ideal crystal structure prediction code?

Cited by 93 publications

References 81 publications

Calculation of Diamagnetic Susceptibility Tensors of Organic Crystals: From Coronene to Pharmaceutical Polymorphs

Calculation of Diamagnetic Susceptibility Tensors of Organic Crystals: From Coronene to Pharmaceutical Polymorphs

Voronoi‐Based Similarity Distances between Arbitrary Crystal Lattices

Substituent Effects in the Crystal Packing of Derivatives of 4′-Phenyl-2,2′:6′,2″-Terpyridine

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