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Dudek MK, Wielgus E, Paluch P, Śniechowska J, Kostrzewa M, Day GM, Bujacz GD, Potrzebowski MJ. Understanding the formation of apremilast cocrystals. ACTA CRYSTALLOGRAPHICA SECTION B, STRUCTURAL SCIENCE, CRYSTAL ENGINEERING AND MATERIALS 2019; 75:803-814. [PMID: 32830759 DOI: 10.1107/s205252061900917x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 06/26/2019] [Indexed: 06/11/2023]
Abstract
Apremilast (APR), an anti-psoriatic agent, easily forms isostructural cocrystals and solvates with aromatic entities, often disobeying at the same time Kitaigorodsky's rule as to the saturation of possible hydrogen-bonding sites. In this paper the reasons for this peculiar behavior are investigated, employing a joint experimental and theoretical approach. This includes the design of cocrystals with coformers having a high propensity towards the formation of both aromatic-aromatic and hydrogen-bonding interactions, determination of their structure, using solid-state NMR spectroscopy and X-ray crystallography, as well as calculations of stabilization energies of formation of the obtained cocrystals, followed by crystal structure prediction calculations and solubility measurements. The findings indicate that the stabilization energies of cocrystal formation are positive in all cases, which results from strain in the APR conformation in these crystal forms. On the other hand, solubility measurements show that the Gibbs free energy of formation of the apremilast:picolinamide cocrystal is negative, suggesting that the formation of the studied cocrystals is entropy driven. This entropic stabilization is associated with the disorder observed in almost all known cocrystals and solvates of APR.
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Greenaway RL, Santolini V, Pulido A, Little MA, Alston BM, Briggs ME, Day GM, Cooper AI, Jelfs KE. From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201909237] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Cui P, McMahon DP, Spackman PR, Alston BM, Little MA, Day GM, Cooper AI. Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights. Chem Sci 2019; 10:9988-9997. [PMID: 32055355 PMCID: PMC6991173 DOI: 10.1039/c9sc02832c] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/19/2019] [Indexed: 11/21/2022] Open
Abstract
New crystal forms of two well-studied organic molecules are identified in a computationally targeted way, by combining structure prediction with a robotic crystallisation screen, including a ‘hidden’ porous polymorph of trimesic acid.
Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new ‘hidden’ porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational–experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.
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Hofstetter A, Balodis M, Paruzzo FM, Widdifield CM, Stevanato G, Pinon AC, Bygrave PJ, Day GM, Emsley L. Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints. J Am Chem Soc 2019; 141:16624-16634. [PMID: 31117663 PMCID: PMC7540916 DOI: 10.1021/jacs.9b03908] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
NMR-based crystallography approaches involving the combination of crystal structure prediction methods, ab initio calculated chemical shifts and solid-state NMR experiments are powerful methods for crystal structure determination of microcrystalline powders. However, currently structural information obtained from solid-state NMR is usually included only after a set of candidate crystal structures has already been independently generated, starting from a set of single-molecule conformations. Here, we show with the case of ampicillin that this can lead to failure of structure determination. We propose a crystal structure determination method that includes experimental constraints during conformer selection. In order to overcome the problem that experimental measurements on the crystalline samples are not obviously translatable to restrict the single-molecule conformational space, we propose constraints based on the analysis of absent cross-peaks in solid-state NMR correlation experiments. We show that these absences provide unambiguous structural constraints on both the crystal structure and the gas-phase conformations, and therefore can be used for unambiguous selection. The approach is parametrized on the crystal structure determination of flutamide, flufenamic acid, and cocaine, where we reduce the computational cost by around 50%. Most importantly, the method is then shown to correctly determine the crystal structure of ampicillin, which would have failed using current methods because it adopts a high-energy conformer in its crystal structure. The average positional RMSE on the NMR powder structure is ⟨rav⟩ = 0.176 Å, which corresponds to an average equivalent displacement parameter Ueq = 0.0103 Å2.
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McDonagh D, Skylaris CK, Day GM. Machine-Learned Fragment-Based Energies for Crystal Structure Prediction. J Chem Theory Comput 2019; 15:2743-2758. [PMID: 30817152 DOI: 10.1021/acs.jctc.9b00038] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevent a reliable assessment of the relative thermodynamic stability of potential structures, while the cost of fully quantum mechanical approaches can limit applications of the methods. We present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach and predicting these corrections with machine learning. Corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve with the fragment corrections. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results using as little as 10-20% of the data for training, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of more widespread use of fragment-based methods in crystal structure prediction, whose increased accuracy at a low computational cost will benefit applications in areas such as polymorph screening and computer-guided materials discovery.
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31
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Dudek MK, Day GM. Explaining crystallization preferences of two polyphenolic diastereoisomers by crystal structure prediction. CrystEngComm 2019. [DOI: 10.1039/c8ce01783b] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Crystal structure prediction is used to understand the differences in crystallization of catechin and epicatechin, and to explore the predictability of solvate formation.
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McMahon DP, Stephenson A, Chong SY, Little MA, Jones JTA, Cooper AI, Day GM. Computational modelling of solvent effects in a prolific solvatomorphic porous organic cage. Faraday Discuss 2018; 211:383-399. [PMID: 30083695 PMCID: PMC6208051 DOI: 10.1039/c8fd00031j] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 03/22/2018] [Indexed: 11/21/2022]
Abstract
Crystal structure prediction methods can enable the in silico design of functional molecular crystals, but solvent effects can have a major influence on relative lattice energies, sometimes thwarting predictions. This is particularly true for porous solids, where solvent included in the pores can have an important energetic contribution. We present a Monte Carlo solvent insertion procedure for predicting the solvent filling of porous structures from crystal structure prediction landscapes, tested using a highly solvatomorphic porous organic cage molecule, CC1. Using this method, we can understand why the predicted global energy minimum structure for CC1 is never observed from solvent crystallisation. We also explain the formation of three different solvatomorphs of CC1 from three structurally-similar chlorinated solvents. Calculated solvent stabilisation energies are found to correlate with experimental results from thermogravimetric analysis, suggesting a future computational framework for a priori materials design that factors in solvation effects.
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LeBlanc LM, Dale SG, Taylor CR, Becke AD, Day GM, Johnson ER. Pervasive Delocalisation Error Causes Spurious Proton Transfer in Organic Acid-Base Co-Crystals. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201809381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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LeBlanc LM, Dale SG, Taylor CR, Becke AD, Day GM, Johnson ER. Pervasive Delocalisation Error Causes Spurious Proton Transfer in Organic Acid-Base Co-Crystals. Angew Chem Int Ed Engl 2018; 57:14906-14910. [PMID: 30248221 DOI: 10.1002/anie.201809381] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Indexed: 11/12/2022]
Abstract
Dispersion-corrected density-functional theory (DFT-D) methods have become the workhorse of many computational protocols for molecular crystal structure prediction due to their efficiency and convenience. However, certain limitations of DFT, such as delocalisation error, are often overlooked or are too expensive to remedy in solid-state applications. This error can lead to artificial stabilisation of charge transfer and, in this work, it is found to affect the correct identification of the protonation site in multicomponent acid-base crystals. As such, commonly used DFT-D methods cannot be applied with any reliability to the study of acid-base co-crystals or salts, while hybrid functionals remain too restrictive for routine use. This presents an impetus for the development of new functionals with reduced delocalisation error for solid-state applications; the structures studied herein constitute an excellent benchmark for this purpose.
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Day GM, Cooper AI. Energy-Structure-Function Maps: Cartography for Materials Discovery. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2018; 30:e1704944. [PMID: 29205536 DOI: 10.1002/adma.201704944] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 09/20/2017] [Indexed: 06/07/2023]
Abstract
Some of the most successful approaches to structural design in materials chemistry have exploited strong directional bonds, whose geometric reliability lends predictability to solid-state assembly. For example, metal-organic frameworks are an important design platform in materials chemistry. By contrast, the structure of molecular crystals is defined by a balance of weaker intermolecular forces, and small changes to the molecular building blocks can lead to large changes in crystal packing. Hence, empirical rules are inherently less reliable for engineering the structures of molecular solids. Energy-structure-function (ESF) maps are a new approach for the discovery of functional organic crystals. These maps fuse crystal-structure prediction with the computation of physical properties to allow researchers to choose the most promising molecule for a given application, prior to its synthesis. ESF maps were used recently to discover a highly porous molecular crystal that has a high methane deliverable capacity and the lowest density molecular crystal reported to date (r = 0.41 g cm-3 , SABET = 3425 m2 g-1 ). Progress in this field is reviewed, with emphasis on the future opportunities and challenges for a design strategy based on computed ESF maps.
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Jung DŠ, Halasz I, McDonagh D, Day GM. Combining experimental and computational techniques for polymorph screening. Acta Crystallogr A Found Adv 2018. [DOI: 10.1107/s0108767318096976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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37
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Jie K, Liu M, Zhou Y, Little MA, Pulido A, Chong SY, Stephenson A, Hughes AR, Sakakibara F, Ogoshi T, Blanc F, Day GM, Huang F, Cooper AI. Near-Ideal Xylene Selectivity in Adaptive Molecular Pillar[ n]arene Crystals. J Am Chem Soc 2018; 140:6921-6930. [PMID: 29754488 PMCID: PMC5997404 DOI: 10.1021/jacs.8b02621] [Citation(s) in RCA: 152] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
The
energy-efficient separation of alkylaromatic compounds is a
major industrial sustainability challenge. The use of selectively
porous extended frameworks, such as zeolites or metal–organic
frameworks, is one solution to this problem. Here, we studied a flexible
molecular material, perethylated pillar[n]arene crystals
(n = 5, 6), which can be used to separate C8 alkylaromatic
compounds. Pillar[6]arene is shown to separate para-xylene from its structural isomers, meta-xylene
and ortho-xylene, with 90% specificity in the solid
state. Selectivity is an intrinsic property of the pillar[6]arene
host, with the flexible pillar[6]arene cavities adapting during adsorption
thus enabling preferential adsorption of para-xylene
in the solid state. The flexibility of pillar[6]arene as a solid sorbent
is rationalized using molecular conformer searches and crystal structure
prediction (CSP) combined with comprehensive characterization by X-ray
diffraction and 13C solid-state NMR spectroscopy. The CSP
study, which takes into account the structural variability of pillar[6]arene,
breaks new ground in its own right and showcases the feasibility of
applying CSP methods to understand and ultimately to predict the behavior
of soft, adaptive molecular crystals.
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Musil F, De S, Yang J, Campbell JE, Day GM, Ceriotti M. Machine learning for the structure-energy-property landscapes of molecular crystals. Chem Sci 2018; 9:1289-1300. [PMID: 29675175 PMCID: PMC5887104 DOI: 10.1039/c7sc04665k] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 12/11/2017] [Indexed: 12/18/2022] Open
Abstract
Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol-1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.
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Taylor C, Day GM. Evaluating the Energetic Driving Force for Cocrystal Formation. CRYSTAL GROWTH & DESIGN 2018; 18:892-904. [PMID: 29445316 PMCID: PMC5806084 DOI: 10.1021/acs.cgd.7b01375] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 12/11/2017] [Indexed: 05/29/2023]
Abstract
We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol-1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems-hydrogen-bonded, halogen-bonded, and weakly bound cocrystals-finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition-more densely packed cocrystals with stronger hydrogen bonding tend to be more stable-but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems.
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Burger V, Claeyssens F, Davies DW, Day GM, Dyer MS, Hare A, Li Y, Mellot-Draznieks C, Mitchell JBO, Mohamed S, Oganov AR, Price SL, Ruggiero M, Ryder MR, Sastre G, Schön JC, Spackman P, Woodley SM, Zhu Q. Applications of crystal structure prediction – inorganic and network structures: general discussion. Faraday Discuss 2018; 211:613-642. [DOI: 10.1039/c8fd90034e] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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41
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Addicoat M, Adjiman CS, Arhangelskis M, Beran GJO, Bowskill D, Brandenburg JG, Braun DE, Burger V, Cole J, Cruz-Cabeza AJ, Day GM, Deringer VL, Guo R, Hare A, Helfferich J, Hoja J, Iuzzolino L, Jobbins S, Marom N, McKay D, Mitchell JBO, Mohamed S, Neumann M, Nilsson Lill S, Nyman J, Oganov AR, Piaggi P, Price SL, Reutzel-Edens S, Rietveld I, Ruggiero M, Ryder MR, Sastre G, Schön JC, Taylor C, Tkatchenko A, Tsuzuki S, van den Ende J, Woodley SM, Woollam G, Zhu Q. Crystal structure evaluation: calculating relative stabilities and other criteria: general discussion. Faraday Discuss 2018; 211:325-381. [DOI: 10.1039/c8fd90031k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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42
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Pinter EN, Cantrell LS, Day GM, Wheeler KA. Pasteur's tartaramide/malamide quasiracemates: new entries and departures from near inversion symmetry. CrystEngComm 2018. [DOI: 10.1039/c8ce00791h] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reinvestigating Pasteur's 1853 quasiracemates has led to unexpected departures from centrosymmetric crystal packing and new insight into the role of molecular shape to molecular assembly.
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Adjiman CS, Brandenburg JG, Braun DE, Cole J, Collins C, Cooper AI, Cruz-Cabeza AJ, Day GM, Dudek M, Hare A, Iuzzolino L, McKay D, Mitchell JBO, Mohamed S, Neelamraju S, Neumann M, Nilsson Lill S, Nyman J, Oganov AR, Price SL, Pulido A, Reutzel-Edens S, Rietveld I, Ruggiero MT, Schön JC, Tsuzuki S, van den Ende J, Woollam G, Zhu Q. Applications of crystal structure prediction – organic molecular structures: general discussion. Faraday Discuss 2018; 211:493-539. [DOI: 10.1039/c8fd90032a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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44
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Addicoat M, Adjiman CS, Arhangelskis M, Beran GJO, Brandenburg JG, Braun DE, Burger V, Burow A, Collins C, Cooper A, Day GM, Deringer VL, Dyer MS, Hare A, Jelfs KE, Keupp J, Konstantinopoulos S, Li Y, Ma Y, Marom N, McKay D, Mellot-Draznieks C, Mohamed S, Neumann M, Nilsson Lill S, Nyman J, Oganov AR, Price SL, Reutzel-Edens S, Ruggiero M, Sastre G, Schmid R, Schmidt J, Schön JC, Spackman P, Tsuzuki S, Woodley SM, Yang S, Zhu Q. Structure searching methods: general discussion. Faraday Discuss 2018; 211:133-180. [DOI: 10.1039/c8fd90030b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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45
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Slater AG, Reiss PS, Pulido A, Little MA, Holden DL, Chen L, Chong SY, Alston BM, Clowes R, Haranczyk M, Briggs ME, Hasell T, Day GM, Cooper AI. Computationally-Guided Synthetic Control over Pore Size in Isostructural Porous Organic Cages. ACS CENTRAL SCIENCE 2017; 3:734-742. [PMID: 28776015 PMCID: PMC5532722 DOI: 10.1021/acscentsci.7b00145] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Indexed: 05/28/2023]
Abstract
The physical properties of 3-D porous solids are defined by their molecular geometry. Hence, precise control of pore size, pore shape, and pore connectivity are needed to tailor them for specific applications. However, for porous molecular crystals, the modification of pore size by adding pore-blocking groups can also affect crystal packing in an unpredictable way. This precludes strategies adopted for isoreticular metal-organic frameworks, where addition of a small group, such as a methyl group, does not affect the basic framework topology. Here, we narrow the pore size of a cage molecule, CC3, in a systematic way by introducing methyl groups into the cage windows. Computational crystal structure prediction was used to anticipate the packing preferences of two homochiral methylated cages, CC14-R and CC15-R, and to assess the structure-energy landscape of a CC15-R/CC3-S cocrystal, designed such that both component cages could be directed to pack with a 3-D, interconnected pore structure. The experimental gas sorption properties of these three cage systems agree well with physical properties predicted by computational energy-structure-function maps.
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Pulido A, Slater AG, Chen L, Little MA, Chong SY, Holden D, Kaczorowski T, Slater BJ, McMahon DP, Cooper AI, Day GM. Computer-guided porous materials design: from rationalization to prediction. Acta Crystallogr A Found Adv 2017. [DOI: 10.1107/s010876731709715x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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47
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Pulido A, Chen L, Kaczorowski T, Holden D, Little MA, Chong SY, Slater BJ, McMahon DP, Bonillo B, Stackhouse CJ, Stephenson A, Kane CM, Clowes R, Hasell T, Cooper AI, Day GM. Functional materials discovery using energy-structure-function maps. Nature 2017; 543:657-664. [PMID: 28329756 PMCID: PMC5458805 DOI: 10.1038/nature21419] [Citation(s) in RCA: 241] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 01/20/2017] [Indexed: 12/24/2022]
Abstract
Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal-organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy-structure-function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy-structure-function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties.
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Selent M, Nyman J, Roukala J, Ilczyszyn M, Oilunkaniemi R, Bygrave PJ, Laitinen R, Jokisaari J, Day GM, Lantto P. Inside Back Cover: Clathrate Structure Determination by Combining Crystal Structure Prediction with Computational and Experimental 129
Xe NMR Spectroscopy (Chem. Eur. J. 22/2017). Chemistry 2017. [DOI: 10.1002/chem.201700348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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49
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Selent M, Nyman J, Roukala J, Ilczyszyn M, Oilunkaniemi R, Bygrave PJ, Laitinen R, Jokisaari J, Day GM, Lantto P. Clathrate Structure Determination by Combining Crystal Structure Prediction with Computational and Experimental 129 Xe NMR Spectroscopy. Chemistry 2017; 23:5258-5269. [PMID: 28111848 PMCID: PMC5763392 DOI: 10.1002/chem.201604797] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Indexed: 11/09/2022]
Abstract
An approach is presented for the structure determination of clathrates using NMR spectroscopy of enclathrated xenon to select from a set of predicted crystal structures. Crystal structure prediction methods have been used to generate an ensemble of putative structures of o- and m-fluorophenol, whose previously unknown clathrate structures have been studied by 129 Xe NMR spectroscopy. The high sensitivity of the 129 Xe chemical shift tensor to the chemical environment and shape of the crystalline cavity makes it ideal as a probe for porous materials. The experimental powder NMR spectra can be used to directly confirm or reject hypothetical crystal structures generated by computational prediction, whose chemical shift tensors have been simulated using density functional theory. For each fluorophenol isomer one predicted crystal structure was found, whose measured and computed chemical shift tensors agree within experimental and computational error margins and these are thus proposed as the true fluorophenol xenon clathrate structures.
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50
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Evans JD, Jelfs KE, Day GM, Doonan CJ. Application of computational methods to the design and characterisation of porous molecular materials. Chem Soc Rev 2017; 46:3286-3301. [DOI: 10.1039/c7cs00084g] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Composed from discrete units, porous molecular materials (PMMs) possess properties not observed for conventional, extended solids. Molecular simulations provide crucial understanding for the design and characterisation of these unique materials.
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