1
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O'Shaughnessy M, Glover J, Hafizi R, Barhi M, Clowes R, Chong SY, Argent SP, Day GM, Cooper AI. Porous isoreticular non-metal organic frameworks. Nature 2024; 630:102-108. [PMID: 38778105 PMCID: PMC11153147 DOI: 10.1038/s41586-024-07353-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/26/2024] [Indexed: 05/25/2024]
Abstract
Metal-organic frameworks (MOFs) are useful synthetic materials that are built by the programmed assembly of metal nodes and organic linkers1. The success of MOFs results from the isoreticular principle2, which allows families of structurally analogous frameworks to be built in a predictable way. This relies on directional coordinate covalent bonding to define the framework geometry. However, isoreticular strategies do not translate to other common crystalline solids, such as organic salts3-5, in which the intermolecular ionic bonding is less directional. Here we show that chemical knowledge can be combined with computational crystal-structure prediction6 (CSP) to design porous organic ammonium halide salts that contain no metals. The nodes in these salt frameworks are tightly packed ionic clusters that direct the materials to crystallize in specific ways, as demonstrated by the presence of well-defined spikes of low-energy, low-density isoreticular structures on the predicted lattice energy landscapes7,8. These energy landscapes allow us to select combinations of cations and anions that will form thermodynamically stable, porous salt frameworks with channel sizes, functionalities and geometries that can be predicted a priori. Some of these porous salts adsorb molecular guests such as iodine in quantities that exceed those of most MOFs, and this could be useful for applications such as radio-iodine capture9-12. More generally, the synthesis of these salts is scalable, involving simple acid-base neutralization, and the strategy makes it possible to create a family of non-metal organic frameworks that combine high ionic charge density with permanent porosity.
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Affiliation(s)
- Megan O'Shaughnessy
- Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Joseph Glover
- Computational System Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Roohollah Hafizi
- Computational System Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Mounib Barhi
- Albert Crewe Centre for Electron Microscopy, University of Liverpool, Liverpool, UK
| | - Rob Clowes
- Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Samantha Y Chong
- Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK
| | | | - Graeme M Day
- Computational System Chemistry, School of Chemistry, University of Southampton, Southampton, UK.
| | - Andrew I Cooper
- Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK.
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2
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Pyzer-Knapp EO, Chen L, Day GM, Cooper AI. Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps. SCIENCE ADVANCES 2021; 7:7/33/eabi4763. [PMID: 34389543 PMCID: PMC8363149 DOI: 10.1126/sciadv.abi4763] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/30/2021] [Indexed: 05/29/2023]
Abstract
While energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving more than 500,000 central processing unit hours from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultrahigh-throughput screening pipelines by greatly reducing the opportunity risk associated with the choice of system to calculate.
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Affiliation(s)
| | - Linjiang Chen
- Leverhulme Research Centre for Functional Materials Design, Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
| | - Graeme M Day
- School of Chemistry, University of Southampton, Southampton, UK
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
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3
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Bowskill DH, Sugden IJ, Konstantinopoulos S, Adjiman CS, Pantelides CC. Crystal Structure Prediction Methods for Organic Molecules: State of the Art. Annu Rev Chem Biomol Eng 2021; 12:593-623. [PMID: 33770462 DOI: 10.1146/annurev-chembioeng-060718-030256] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade.
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Affiliation(s)
- David H Bowskill
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom;
| | - Isaac J Sugden
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom;
| | - Stefanos Konstantinopoulos
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom;
| | - Claire S Adjiman
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom;
| | - Constantinos C Pantelides
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom;
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4
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Zhu Q, Gu Y, Hu L, Gaudin T, Fan M, Ma J. Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields. J Chem Phys 2021; 154:074502. [PMID: 33607909 DOI: 10.1063/5.0038267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Viscosity of organic liquids is an important physical property in applications of printing, pharmaceuticals, oil extracting, engineering, and chemical processes. Experimental measurement is a direct but time-consuming process. Accurately predicting the viscosity with a broad range of chemical diversity is still a great challenge. In this work, a protocol named Variable Force Field (VaFF) was implemented to efficiently vary the force field parameters, especially λvdW, for the van der Waals term for the shear viscosity prediction of 75 organic liquid molecules with viscosity ranging from -9 to 0 in their nature logarithm and containing diverse chemical functional groups, such as alcoholic hydroxyl, carbonyl, and halogenated groups. Feature learning was applied for the viscosity prediction, and the selected features indicated that the hydrogen bonding interactions and the number of atoms and rings play important roles in the property of viscosity. The shear viscosity prediction of alcohols is very difficult owing to the existence of relative strong intermolecular hydrogen bonding interaction as reflected by density functional theory binding energies. From radial and spatial distribution functions of methanol, we found that the van der Waals related parameters λvdW are more crucial to the viscosity prediction than the rotation related parameters, λtor. With the variable λvdW-based all-atom optimized potentials for liquid simulations force field, a great improvement was observed in the viscosity prediction for alcohols. The simplicity and uniformity of VaFF make it an efficient tool for the prediction of viscosity and other related properties in the rational design of materials with the specific properties.
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Affiliation(s)
- Qiang Zhu
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
| | - Yuming Gu
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
| | - Limu Hu
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
| | - Théophile Gaudin
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
| | - Mengting Fan
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China
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5
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Zhao C, Chen L, Che Y, Pang Z, Wu X, Lu Y, Liu H, Day GM, Cooper AI. Digital navigation of energy-structure-function maps for hydrogen-bonded porous molecular crystals. Nat Commun 2021; 12:817. [PMID: 33547307 PMCID: PMC7865007 DOI: 10.1038/s41467-021-21091-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/12/2021] [Indexed: 11/24/2022] Open
Abstract
Energy-structure-function (ESF) maps can aid the targeted discovery of porous molecular crystals by predicting the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of rigid molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. We show that the positioning of the hydrogen-bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps, revealing promising structure-function spaces for future experiments. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map, enabling an efficient navigation of the ESF data to identify 'landmark' structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, an important goal for closed-loop, autonomous searches for molecular crystals with useful functions.
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Affiliation(s)
- Chengxi Zhao
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Linjiang Chen
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China.
| | - Yu Che
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Zhongfu Pang
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Xiaofeng Wu
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Yunxiang Lu
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Honglai Liu
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK.
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China.
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6
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Yang J, Li N, Li S. The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes. CrystEngComm 2019. [DOI: 10.1039/c9ce01190k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures.
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Affiliation(s)
- Jack Yang
- Advanced Materials and Manufacturing Futures Institute
- School of Material Science and Engineering
- University of New South Wales
- Sydney
- Australia
| | - Nathan Li
- Advanced Materials and Manufacturing Futures Institute
- School of Material Science and Engineering
- University of New South Wales
- Sydney
- Australia
| | - Sean Li
- Advanced Materials and Manufacturing Futures Institute
- School of Material Science and Engineering
- University of New South Wales
- Sydney
- Australia
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7
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van de Streek J, Alig E, Parsons S, Vella-Zarb L. A jumping crystal predicted with molecular dynamics and analysed with TLS refinement against powder diffraction data. IUCRJ 2019; 6:136-144. [PMID: 30713711 PMCID: PMC6327187 DOI: 10.1107/s205225251801686x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 11/27/2018] [Indexed: 06/09/2023]
Abstract
By running a temperature series of molecular dynamics (MD) simulations starting from the known low-temperature phase, the experimentally observed phase transition in a 'jumping crystal' was captured, thereby providing a prediction of the unknown crystal structure of the high-temperature phase and clarifying the phase-transition mechanism. The phase transition is accompanied by a discontinuity in two of the unit-cell parameters. The structure of the high-temperature phase is very similar to that of the low-temperature phase. The anisotropic displacement parameters calculated from the MD simulations readily identified libration as the driving force behind the phase transition. Both the predicted crystal structure and the phase-transition mechanism were verified experimentally using TLS (translation, libration, screw) refinement against X-ray powder diffraction data.
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Affiliation(s)
- Jacco van de Streek
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
- Institute for Inorganic and Analytical Chemistry, Goethe-University Frankfurt, Frankfurt am Main, Germany
| | - Edith Alig
- Institute for Inorganic and Analytical Chemistry, Goethe-University Frankfurt, Frankfurt am Main, Germany
| | - Simon Parsons
- School of Chemistry/Centre for Science at Extreme Conditions, University of Edinburgh, Edinburgh, UK
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8
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Gatsiou CA, Adjiman CS, Pantelides CC. Repulsion-dispersion parameters for the modelling of organic molecular crystals containing N, O, S and Cl. Faraday Discuss 2018; 211:297-323. [PMID: 30094433 DOI: 10.1039/c8fd00064f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
In lattice energy models that combine ab initio and empirical components, it is important to ensure consistency between these components so that meaningful quantitative results are obtained. A method for deriving parameters of atom-atom repulsion dispersion potentials for crystals, tailored to different ab initio models, is presented. It is based on minimization of the sum of squared deviations between experimental and calculated structures and energies. The solution algorithm is designed to avoid convergence to local minima in the parameter space by combining a deterministic low-discrepancy sequence for the generation of multiple initial parameter guesses with an efficient local minimization algorithm. The proposed approach is applied to derive transferable exp-6 potential parameters suitable for use in conjunction with a distributed multipole electrostatics model derived from isolated molecule charge densities calculated at the M06/6-31G(d,p) level of theory. Data for hydrocarbons, azahydrocarbons, oxohydrocarbons, organosulphur compounds and chlorohydrocarbons are used for the estimation. A good fit is achieved for the new set of parameters with a mean absolute error in sublimation enthalpies of 4.1 kJ mol-1 and an average rmsd15 of 0.31 Å. The parameters are found to perform well on a separate cross-validation set of 39 compounds.
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Affiliation(s)
- Christina A Gatsiou
- Molecular Systems Engineering Group, Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
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9
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Hofmann D, Kuleshova L. Empirical temperature-dependent intermolecular potentials determined by data mining from crystal data. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.03.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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10
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Nyman J, Reutzel-Edens S. Crystal structure prediction is changing from basic science to applied technology. Faraday Discuss 2018; 211:459-476. [DOI: 10.1039/c8fd00033f] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Prediction of true polymorphs as dynamic ensembles in contrast to hypothetical static crystal structures.
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Affiliation(s)
- Jonas Nyman
- School of Pharmacy
- University of Wisconsin – Madison
- Madison
- USA
- Small Molecule Design & Development
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11
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McDonagh JL, Silva AF, Vincent MA, Popelier PLA. Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms. J Chem Theory Comput 2017; 14:216-224. [PMID: 29211469 DOI: 10.1021/acs.jctc.7b01157] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
We present an innovative method for predicting the dynamic electron correlation energy of an atom or a bond in a molecule utilizing topological atoms. Our approach uses the machine learning method Kriging (Gaussian Process Regression with a non-zero mean function) to predict these dynamic electron correlation energy contributions. The true energy values are calculated by partitioning the MP2 two-particle density-matrix via the Interacting Quantum Atoms (IQA) procedure. To our knowledge, this is the first time such energies have been predicted by a machine learning technique. We present here three important proof-of-concept cases: the water monomer, the water dimer, and the van der Waals complex H2···He. These cases represent the final step toward the design of a full IQA potential for molecular simulation. This final piece will enable us to consider situations in which dispersion is the dominant intermolecular interaction. The results from these examples suggest a new method by which dispersion potentials for molecular simulation can be generated.
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Affiliation(s)
- James L McDonagh
- Manchester Institute of Biotechnology, The University of Manchester , 131 Princess Street, Manchester M1 7DN, Great Britain
| | - Arnaldo F Silva
- Manchester Institute of Biotechnology, The University of Manchester , 131 Princess Street, Manchester M1 7DN, Great Britain
| | - Mark A Vincent
- School of Chemistry, The University of Manchester , Oxford Road, Manchester M13 9PL, Great Britain
| | - Paul L A Popelier
- Manchester Institute of Biotechnology, The University of Manchester , 131 Princess Street, Manchester M1 7DN, Great Britain.,School of Chemistry, The University of Manchester , Oxford Road, Manchester M13 9PL, Great Britain
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12
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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: 245] [Impact Index Per Article: 35.0] [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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Affiliation(s)
- Angeles Pulido
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Linjiang Chen
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | | | - Daniel Holden
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Marc A Little
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | | | | | - David P McMahon
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | | | | | | | | | - Rob Clowes
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Tom Hasell
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Andrew I Cooper
- Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
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13
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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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Affiliation(s)
- Marcin Selent
- NMR Research Unit, Faculty of Science, University of Oulu, 90014, Oulu, Finland.,Faculty of Chemistry, Wrocław University, Joliot Curie 14, 50-383, Wrocław, Poland
| | - Jonas Nyman
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Juho Roukala
- NMR Research Unit, Faculty of Science, University of Oulu, 90014, Oulu, Finland
| | - Marek Ilczyszyn
- Faculty of Chemistry, Wrocław University, Joliot Curie 14, 50-383, Wrocław, Poland
| | - Raija Oilunkaniemi
- Laboratory of Inorganic Chemistry, University of Oulu, 90014, Oulu, Finland
| | - Peter J Bygrave
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Risto Laitinen
- Laboratory of Inorganic Chemistry, University of Oulu, 90014, Oulu, Finland
| | - Jukka Jokisaari
- NMR Research Unit, Faculty of Science, University of Oulu, 90014, Oulu, Finland
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK
| | - Perttu Lantto
- NMR Research Unit, Faculty of Science, University of Oulu, 90014, Oulu, Finland
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14
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Fedorov AY, Rychkov DA, Losev EA, Zakharov BA, Stare J, Boldyreva EV. Effect of pressure on two polymorphs of tolazamide: why no interconversion? CrystEngComm 2017. [DOI: 10.1039/c6ce02527g] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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15
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Rychkov DA, Stare J, Boldyreva EV. Pressure-driven phase transition mechanisms revealed by quantum chemistry: l-serine polymorphs. Phys Chem Chem Phys 2017; 19:6671-6676. [PMID: 28210731 DOI: 10.1039/c6cp07721h] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The present study delivers a computational approach for the understanding of the mechanism of phase transitions between polymorphs of small organic molecules.
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Affiliation(s)
- Denis A. Rychkov
- Institute of Solid State Chemistry and Mechanochemistry
- Siberian Branch of Russian Academy of Sciences
- Novosibirsk 630128
- Russian Federation
- Novosibirsk State University
| | - Jernej Stare
- National Institute of Chemistry
- Ljubljana
- Slovenia
| | - Elena V. Boldyreva
- Institute of Solid State Chemistry and Mechanochemistry
- Siberian Branch of Russian Academy of Sciences
- Novosibirsk 630128
- Russian Federation
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16
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Nyman J, Day GM. Modelling temperature-dependent properties of polymorphic organic molecular crystals. Phys Chem Chem Phys 2016; 18:31132-31143. [PMID: 27812563 PMCID: PMC5299590 DOI: 10.1039/c6cp05447a] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 10/31/2016] [Indexed: 12/17/2022]
Abstract
We present a large-scale study of the temperature-dependence of structures, free energy differences and properties of polymorphic molecular organic crystals. Lattice-vibrational Gibbs free energy differences between 475 pairs of polymorphs of organic molecular crystals have been calculated at 0 K and at their respective melting points, using a highly accurate anisotropic multipole-based force field and including thermal expansion through the use of a (negative) thermal pressure. Re-ranking of the relative thermodynamic stability of the polymorphs in each pair indicates the possibility of an enantiotropic phase transition between the crystal structures, which occurs in 21% of the studied systems. While vibrational contributions to free energies can have a significant effect on thermodynamic stability, the impact of thermal expansion on polymorph free energy differences is generally very small. We also calculate thermal expansion coefficients for the 864 crystal structures and investigate the temperature-dependence of mechanical properties, and pairwise differences in these properties between polymorphs.
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Affiliation(s)
- Jonas Nyman
- School of Chemistry, University of Southampton, Southampton, UK.
| | - Graeme M Day
- School of Chemistry, University of Southampton, Southampton, UK.
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17
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Day GM, Görbitz CH. Introduction to the special issue on crystal structure prediction. ACTA CRYSTALLOGRAPHICA SECTION B, STRUCTURAL SCIENCE, CRYSTAL ENGINEERING AND MATERIALS 2016; 72:435-436. [PMID: 27484366 DOI: 10.1107/s2052520616012348] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Graeme M Day
- Department of Chemistry, University of Southampton, Highfield Campus, Southampton, Hampshire SO17 1BJ, England
| | - Carl Henrik Görbitz
- Department of Chemistry, University of Oslo, PO Box 1033 Blindern, N-0315 Oslo, Norway
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