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.

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 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.

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

CrystEngComm ◽  
2019 ◽  
Vol 21 (41) ◽  
pp. 6173-6185 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Machine Learning ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Energy Landscapes ◽  
Unsupervised Machine Learning

Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures.

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.

scholarly journals Predicted crystal energy landscapes of porous organic cages

2014 ◽  
Vol 5 (6) ◽  
pp. 2235-2245 ◽  
Author(s):  
Edward O. Pyzer-Knapp ◽  
Hugh P. G. Thompson ◽  
Florian Schiffmann ◽  
Kim E. Jelfs ◽  
Samantha Y. Chong ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Computational Methods ◽  
Crystal Packing ◽  
Energy Landscapes ◽  
Cage Molecules

Computational methods predict the crystal packing of porous organic cage molecules, allowing crystal structure and porosity to be predicted starting from the chemical diagram alone.

scholarly journals Reactions of copper(II) bromide with 2,6-diacetylpyridine bis(phenyl-hydrazone) (L)-molecular and crystal structure of L and its mixed-valence complex [cuiil2][cui2br4]

2021 ◽  
pp. 112-112
Author(s):  
Marko Rodic ◽  
Mirjana Radanovic ◽  
Dragana Gazdic ◽  
Vukadin Leovac ◽  
Berta Barta-Holló ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Thermal Stability ◽  
Crystal Packing ◽  
Complex Cation ◽  
Mixed Valence ◽  
Molecular Structures ◽  
Planar Geometry ◽  
Dispersion Interactions ◽  
X Ray Crystallography ◽  
Thermal Stability Of

Utilizing X-ray crystallography the crystal and molecular structures of 2,6-diacetylpyridine bis(phenylhydrazone) (L) were determined. Energetics of the intermolecular interactions in the crystal structure was assessed with computational methods, revealing that dispersion interactions are dominant. The basic structural unit of the crystal packing is revealed to be the herring-bone type arrangement of L molecules. Assignation of the IR spectrum of L with the aid of DFT calculations was performed. Furthermore, new reactions of L with CuBr2 in different solvents are described, which led to the synthesis of the mixed Cu(II)-Cu(I) complex of the formula [CuIIL2][CuI2Br4] (1), and its structural characterization. In the complex cation, two molecules of tridentate N3 ligand are meridionally arranged in a very distorted octahedral environment of a Cu(II) ion. In [Cu2Br4]2-, bromide ions are arranged in a trigonal-planar geometry around each copper(I) atom. Finally, for the ligand, 1, and the previously synthesized complex [CuL2]Br2, thermal properties were examined. The thermal stability of the com-plexes is lower than that of the ligand and decreases in order: L (250?C) > > [CuL2]Br2 (221?C) > [CuIIL2][CuI2Br4] (212?C). The differences in thermal stability of the complexes are due to differences in packing efficacy of the constitutional ions.

scholarly journals Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

2018 ◽  
Author(s):  
Daniel Elton ◽  
Zois Boukouvalas ◽  
Mark S. Butrico ◽  
Mark D. Fuge ◽  
Peter W. Chung
Keyword(s):  
Machine Learning ◽  
Energetic Materials ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Small Data ◽  
Detonation Pressure ◽  
Learning Models ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

scholarly journals Synthon robustness by C-F groups in halogen substituted N-benzylideneanilines

2014 ◽  
Vol 70 (a1) ◽  
pp. C1811-C1811
Author(s):  
Gurpreet Kaur ◽  
Angshuman Roy Choudhury
Keyword(s):  
Crystal Structure ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Weak Interactions ◽  
Topological Analysis ◽  
Strong Hydrogen Bond ◽  
Hydrogen Bond Donor ◽  
Donor And Acceptor ◽  
The Impact ◽  
Organic Fluorine

The arrangement of the molecules in their crystal structure is controlled by the non-covalent intermolecular interactions other than the effectual space filling. The role of strong hydrogen bonds in guiding the crystal packing is well-known in the literature. But, how significant are the weak interactions in the field of crystal engineering, has yet not been fully understood. Our aim is to comprehend the nature and strength of the weak interactions involving fluorine in guiding the packing of small organic molecules in their respective crystal structure. The reason being the controversies, which are involved regarding the interactions offered by "organic fluorine"[1] and also due to the importance of these interactions in the pharmaceutical industry. Some of the research groups indicate the incapability of interactions offered by fluorine in the formation of supramolecular motifs, whereas other groups have indicated that substantial role is being played by fluorine in constructing the lattice through C-H···F, C-F···F and C-F···π interactions in the presence and absence of strong hydrogen bond donor and acceptor groups. To understand more about these interactions, we have chosen a model system of halogen substituted N-benzylideneanilines[2]. In this system, we have analysed the impact of fluorine mediated interactions on the crystal packing by having fluorine as a substituent on both the phenyl rings. Then the robustness of the synthons offered by organic fluorine has been anticipated in the same system, but with one of the substituent as chlorine or bromine in either of the phenyl ring. It has been observed that the replacement of the non-interacting fluorine by its heavier analogue has not altered the supramolecular motif, which was formed by the other fluorine. But the crystal packing has been found to be completely altered in the molecules where the interacting fluorine was replaced by its heavier analogue. Salient features of our computational studies, which include the calculation of the stabilization energies of the studied dimers using MP2 method and their topological analysis using AIM2000, to support the experimental observations will also be presented to highlight the sturdiness of the synthons formed by so called "organic fluorine".

scholarly journals Molecular Structures Polymorphism the Role of F…F Interactions in Crystal Packing of Fluorinated Tosylates

Crystals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 242 ◽  
Author(s):  
Dmitry E. Arkhipov ◽  
Alexander V. Lyubeshkin ◽  
Alexander D. Volodin ◽  
Alexander A. Korlyukov
Keyword(s):  
Intermolecular Interactions ◽  
Crystal Packing ◽  
Lattice Energy ◽  
Molecular Structures ◽  
X Ray Diffraction ◽  
Total Contribution ◽  
X Ray ◽  
Weak Hydrogen Bonds ◽  
Intermolecular Bonding

The peculiarities of interatomic interactions formed by fluorine atoms were studied in four tosylate derivatives p-CH3C6H4OSO2CH2CF2CF3 and p-CH3C6H4OSO2CH2(CF2)nCHF2 (n = 1, 5, 7) using X-ray diffraction and quantum chemical calculations. Compounds p-CH3C6H4OSO2CH2(CF2)nCHF2 (n = 1, 5) were crystallized in several polymorph modifications. Analysis of intermolecular bonding was carried out using QTAIM approach and energy partitioning. All compounds are characterized by crystal packing of similar type and the contribution of intermolecular interactions formed by fluorine atoms to lattice energy is raised along with the increase of their amount. The energy of intra- and intermolecular F…F interactions is varied in range 0.5–13.0 kJ/mol. Total contribution of F…F interactions to lattice energy does not exceed 40%. Crystal structures of studied compounds are stabilized mainly by C-H…O and C-H…F weak hydrogen bonds. The analysis of intermolecular interactions and lattice energies in polymorphs of p-CH3C6H4OSO2CH2(CF2)nCHF2 (n = 1, 5) has shown that most stabilized are characterized by the least contribution of F…F interactions.

scholarly journals New Ag(I) Coordination Complexes Based on Bis(1-isoquinolinecarboxamide)ethane.

2014 ◽  
Vol 70 (a1) ◽  
pp. C665-C665
Author(s):  
Nicole Parra ◽  
Julio Belmar ◽  
Claudio Jiménez ◽  
Jorge Pasán ◽  
Catalina Ruiz-Pérez
Keyword(s):  
Crystal Engineering ◽  
Rational Design ◽  
Crystal Packing ◽  
Functional Materials ◽  
Coordination Complexes ◽  
Intermolecular Forces ◽  
Research Area ◽  
Molecular Structures ◽  
Gauche Conformation ◽  
Design And Synthesis

Crystal Engineering is an interdisciplinary research area that involves chemists, physicists, biologists and materials scientists.1It is an important field inside Supramolecular Chemistry which has been considered as a new form of synthesis, named Supramolecular Synthesis.2It is known that important properties in molecular solids are closely related with the way that molecules are aggregated in the condensed phase. Consequently, the ability to control the molecular association in the crystal packing could offer control over specific properties and potential applications. Because of that, the main goal of Crystal Engineering is the rational design and synthesis of functional materials using the nature of the intermolecular forces as a toolkit. Our strategy is the systematic study of non-covalent forces in homologous series.3In this work our interest is focused on the study of crystal packing of two homologous ligands N,N'-bis(1-isoquinolinecarboxamide)-1,2-ethane (1) and N,N'-dimethyl-N,N'-bis(1-isoquinolinecarboxamide)-1,2-ethane (2) and their Ag(I) coordination complexes. The compound 1 consists of two isoquinoline rings and one ethylene bridge linked by amide functional groups. Compound 2 is the result of the N-methylation of 1. The main difference in the molecular structures is that while 1 present a gauche conformation in the 1,2-ethanediamine bridge (600) 2 present a staggered conformation (1800). Curiously, in spite of this fact, the Ag(I) complexes in both cases present a small torsion angle of 4501-Ag(I) and 6502-Ag(I). These orientations allow the torsion of the isoquinoline moiety and the formation of homonuclear 0D coordination complexes, over the 1D coordination polymer expected. The main intermolecular interaction in 1 is the amide-to-amide hydrogen bond that is replaced by a weak CH··O interaction in 2 On the other hand, both Ag(I) complexes use the nitrate counteranion to build a chain using NH··O(nitrate) in 1 and CH(quinoline)··O(nitrate) in 2.Acknowledgment: Grant DIUC 212.023.049-1.0

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