scholarly journals Towards computer-guided tuning of the crystal packing of porous organic cages

2014 ◽  
Vol 70 (a1) ◽  
pp. C667-C667
Author(s):  
Angeles Pulido ◽  
Ming Liu ◽  
Paul Reiss ◽  
Anna Slater ◽  
Sam Chong ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Engineering ◽  
Molecular Sieves ◽  
Structure Prediction ◽  
Crystal Packing ◽  
Aromatic Aldehydes ◽  
Lattice Energy ◽  
Building Blocks ◽  
Molecular Assembly ◽  
Computational Techniques

Among microporous materials, there has been an increasing recent interest in porous organic cage (POC) crystals, which can display permanent intrinsic (molecular) and extrinsic (crystal network) porosity. These materials can be used as molecular sieves for gas separation and potential applications as enzyme mimics have been suggested since they exhibit structural response toward guest molecules[1]. Small structural modifications of the initial building blocks of the porous organic molecules can lead to quite different molecular assembly[1]. Moreover, the crystal packing of POCs is based on weak molecular interactions and is less predictable that other porous materials such as MOFs or zeolites.[2] In this contribution, we show that computational techniques -molecular conformational searches and crystal structure prediction- can be successfully used to understand POC crystal packing preferences. Computational results will be presented for a series of closely related tetrahedral imine- and amine-linked porous molecules, formed by [4+6] condensation of aromatic aldehydes and cyclohexyl linked diamines. While the basic cage is known to have one strongly preferred crystal structure, the presence of small alkyl groups on the POC modifies its crystal packing preferences, leading to extensive polymorphism. Calculations were able to successfully identify these trends as well as to predict the structures obtained experimentally, demonstrating the potential for computational pre-screening in the design of POCs within targeted crystal structures. Moreover, the need of accurate molecular (ab initio calculations) and crystal (based on atom-atom potential lattice energy minimization) modelling for computer-guided crystal engineering will be discussed.

scholarly journals Insight from energy surfaces: structure prediction by lattice energy exploration

2014 ◽  
Vol 70 (a1) ◽  
pp. C28-C28
Author(s):  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Crystal Packing ◽  
Lattice Energy ◽  
Organic Crystals ◽  
Crystal Structure Prediction ◽  
Stability Order ◽  
The Stability ◽  
Solid Form ◽  
Energy Surfaces

A long-standing challenge for the application of computational chemistry in the field of crystallography is the prediction of crystal packing, given no more than the chemical bonding of the molecules being crystallised. Recent years have seen significant progress towards reliable crystal structure prediction methods, even for traditionally challenging systems involving flexible molecules and multi-component solids [1]. These methods are based on global searches of the lattice energy surface: a search is performed to locate all possible packing arrangements, and these structures are ranked by their calculated energy [2]. One aim of this lecture is to provide an overview of advances in methods for crystal structure prediction, focussing on molecular organic crystals, and highlighting strategies that are being explored to extend the reach of these methods to more complex systems. A second aim is to discuss the range applications of crystal structure prediction calculations, which have traditionally included solid form screening, particularly of pharmaceutically active molecules, and structure determination. As energy models become more reliable at correctly ranking the stability order of putative structures, and the timescale required for structure searching decreases, crystal structure prediction has the potential for the discovery of novel molecular materials with targeted properties. Prospects for computer-guided discovery of materials will be discussed.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

Applications of the Cambridge Structural Database in organic chemistry and crystal chemistry

2002 ◽  
Vol 58 (3) ◽  
pp. 407-422 ◽  
Author(s):  
Frank H. Allen ◽  
W. D. Samuel Motherwell
Keyword(s):  
Organic Chemistry ◽  
Crystal Structure ◽  
Crystal Chemistry ◽  
Crystal Engineering ◽  
Structure Prediction ◽  
Crystal Packing ◽  
Cambridge Structural Database ◽  
Software Systems ◽  
Wide Range ◽  
Structural Database

The Cambridge Structural Database (CSD) and its associated software systems have formed the basis for more than 800 research applications in structural chemistry, crystallography and the life sciences. Relevant references, dating from the mid-1970s, and brief synopses of these papers are collected in a database, DBUse, which is freely available via the CCDC website. This database has been used to review research applications of the CSD in organic chemistry, including supramolecular applications, and in organic crystal chemistry. The review concentrates on applications that have been published since 1990 and covers a wide range of topics, including structure correlation, conformational analysis, hydrogen bonding and other intermolecular interactions, studies of crystal packing, extended structural motifs, crystal engineering and polymorphism, and crystal structure prediction. Applications of CSD information in studies of crystal structure precision, the determination of crystal structures from powder diffraction data, together with applications in chemical informatics, are also discussed.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

scholarly journals Contact map based crystal structure prediction using global optimization

CrystEngComm ◽  
2021 ◽  
Author(s):  
Jianjun Hu ◽  
Wenhui Yang ◽  
Rongzhi Dong ◽  
Yuxin Li ◽  
Xiang Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Global Optimization ◽  
Crystal Engineering ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
New Materials ◽  
Contact Map

Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering.

scholarly journals Blind crystal structure prediction of a novel second polymorph of 1-hydroxy-7-azabenzotriazole

2006 ◽  
Vol 62 (4) ◽  
pp. 642-650 ◽  
Author(s):  
Harriott Nowell ◽  
Christopher S. Frampton ◽  
Julie Waite ◽  
Sarah L. Price
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Structure Refinement ◽  
Lattice Energy ◽  
Polymorphic Form ◽  
Crystal Structure Prediction ◽  
Blind Test ◽  
Coupling Reagent ◽  
Energy Penalty ◽  
Alcohol Solvents

The commercially available peptide coupling reagent 1-hydroxy-7-azabenzotriazole has been shown to crystallize in two polymorphic forms. The two polymorphs differ in their hydrogen-bonding motif, with form I having an R_2^2(10) dimer motif and form II having a C(5) chain motif. The previously unreported form II was used as an informal blind test of computational crystal structure prediction for flexible molecules. The crystal structure of form II has been successfully predicted blind from lattice-energy minimization calculations following a series of searches using a large number of rigid conformers. The structure for form II was the third lowest in energy with form I found as the global minimum, with the energy calculated as the sum of the ab initio intramolecular energy penalty for conformational distortion and the intermolecular lattice energy which is calculated from a distributed multipole representation of the charge density. The predicted structure was sufficiently close to the experimental structure that it could be used as a starting model for crystal structure refinement. A subsequent limited polymorph screen failed to yield a third polymorphic form, but demonstrated that alcohol solvents are implicated in the formation of the form I dimer structure.

The lines-of-force landscape of interactions between molecules in crystals; cohesive versus tolerant and `collateral damage' contact

2010 ◽  
Vol 66 (3) ◽  
pp. 396-406 ◽  
Author(s):  
Angelo Gavezzotti
Keyword(s):  
Crystal Structures ◽  
Structure Prediction ◽  
Crystal Packing ◽  
Minimum Energy ◽  
Lattice Energy ◽  
Interaction Force ◽  
Coordination Shell ◽  
Geometrical Parameters ◽  
Interaction Energies ◽  
Organic Salts

A quantitative analysis of relative stabilities in organic crystal structures is possible by means of reliable calculations of interaction energies between pairs of molecules. Such calculations have been performed by the PIXEL method for 1108 non-ionic and 98 ionic organic crystals, yielding total energies and separate Coulombic polarization and dispersive contributions. A classification of molecule–molecule interactions emerges based on pair energy and its first derivative, the interaction force, which is estimated here explicitly along an approximate stretching path. When molecular separation is not at the minimum-energy value, as frequently happens, forces may be attractive or repulsive. This information provides a fine structural fingerprint and may be relevant to the mechanical properties of materials. The calculations show that the first coordination shell includes destabilizing contacts in ∼ 9% of crystal structures for compounds with highly polar chemical groups (e.g. CN, NO2, SO2). Calculations also show many pair contacts with weakly stabilizing (neutral) energies; such fine modulation is presumably what makes crystal structure prediction so difficult. Ionic organic salts or zwitterions, including small peptides, show a Madelung-mode pairing of opposite ions where the total lattice energy is stabilized from sums of strongly repulsive and strongly attractive interactions. No obvious relationships between atom–atom distances and interaction energies emerge, so analyses of crystal packing in terms of geometrical parameters alone should be conducted with due care.

scholarly journals Reducing Crystal Structure Overprediction of Ibuprofen with Large Scale Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Nicholas Francia ◽  
Louise Price ◽  
Matteo Salvalaglio
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Crystal Structures ◽  
Finite Temperature ◽  
Structure Prediction ◽  
Large Scale ◽  
Lattice Energy ◽  
Racemic Mixture ◽  
Dynamics Simulations

<p>The control of the crystal form is a central issue in the pharmaceutical industry. The identification of putative polymorphs through Crystal Structure Prediction (CSP) methods is based on lattice energy calculations, which are known to significantly over-predict the number of plausible crystal structures. A valuable tool to reduce overprediction is to employ physics-based, dynamic simulations to coalesce lattice energy minima separated by small barriers into a smaller number of more stable geometries once thermal effects are introduced. Molecular dynamics simulations and enhanced sampling methods can be employed in this context to simulate crystal structures at finite temperature and pressure. </p><p>Here we demonstrate the applicability of approaches based on molecular dynamics to systematically process realistic CSP datasets containing several hundreds of crystal structures. The system investigated is ibuprofen, a conformationally flexible active pharmaceutical ingredient that crystallises both in enantiopure forms and as a racemic mixture. By introducing a hierarchical approach in the analysis of finite-temperature supercell configurations, we can post-process a dataset of 555 crystal structures, identifying 65% of the initial structures as labile, while maintaining all the experimentally known crystal structures in the final, reduced set. Moreover, the extensive nature of the initial dataset allows one to gain quantitative insight into the persistence and the propensity to transform of crystal structures containing common hydrogen-bonded intermolecular interaction motifs.</p>

scholarly journals From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting

2019 ◽  
Author(s):  
Rebecca L. Greenaway ◽  
Valentina Santolini ◽  
Angeles Pulido ◽  
Marc A. Little ◽  
Ben M. Alston ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Chiral Recognition ◽  
Crystal Packing ◽  
A Priori ◽  
Computational Prediction ◽  
Crystal Structure Prediction ◽  
Social Self ◽  
The Social ◽  
Experimental Crystal

<p>We describe the <i>a priori </i>computational prediction and realization of multi-component cage pots, starting with molecular predictions based on candidate precursors through to crystal structure prediction and synthesis using robotic screening. The molecules were formed by the social self-sorting of a tri-topic aldehyde with both a tri-topic amine and di-topic amine, without using orthogonal reactivity or precursors of the same topicity. Crystal structure prediction suggested a rich polymorphic landscape, where there was an overall preference for chiral recognition to form heterochiral rather than homochiral packings, with heterochiral pairs being more likely to pack window-to-window to form two-component capsules. These crystal packing preferences were then observed in experimental crystal structures. <br></p>

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