Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447

Improving the accuracy of lattice energy calculations in crystal structure prediction using experimental data

Crystal structure prediction (CSP) has been a problem of great industrial interest but also a fundamental challenge in condensed matter science. The problem involves the identification of the stable and metastable crystals of a given compound at certain temperature and pressure conditions. Computational CSP methods based on the lattice energy minimization have been successful in identifying experimentally observed crystals of an organic compound as local minima of the lattice energy landscape but not always with the correct relative stability. This is primarily controlled by the lattice energy model. The lattice energy model adopted in this work is based on the assumption that molecules are rigid, electrostatic interactions are modelled via distributed multipoles derived from the ab initio charge density of the gas phase conformation and an empirical pairwise exp-6 potential for the repulsion dispersion interactions. Based on the fact that the reliability of all computational models is based on their agreement with experimental evidence, the use of available experimental data for improving the lattice energy model is the main focus of this work. First the impact of different modelling choices -- choice of level of theory for electrostatics and parameters for the repulsion-dispersion term -- in the modelling of experimental structures, energies and relative stabilities is investigated. Results suggest that a reestimation of the repulsion-dispersion parameters is expected to produce parameters consistent with changes in the other lattice energy terms and bring the model closer to experiment, consequently improving predictions. An algorithm, CrystalEstimator, for fitting the exp-6 potential parameters by minimizing the sum of squared deviations between experimental structures and energies and the corresponding relaxed structures and energies is developed. The lattice energy of the experimental structures is minimized by the program DMACRYS. The solution algorithm is based on the search of the parameter space using deterministic low discrepancy sequences; and the use of an efficient local minimization algorithm. The proposed method is applied to derive transferable exp-6 potential parameters for hydrocarbons, organosulphur compounds, azahydrocarbons, oxohydrocarbons and organosulphur compounds containing nitrogen. Three different sets of parameters are developed, suitable for use in conjunction with three different models of electrostatics derived at the HF/6-31G(d,p), M06/6-31G(d,p) and MP2/6-31g(d,p) levels. A good fit is achieved for all the new sets of parameters with a mean absolute error in sublimation enthalpies less than 3.5 kJ/mol and an average rmsd15 less than 0.35 Å. Prediction studies are performed for acetylene, tetracyanoethylene and blind test molecule XXII and the generated lattice energy landscapes are refined with the new models. The observed experimental structures are predicted with better structural agreement but the same or higher ranking than those obtained by the previously used FIT parameter set.Open Acces

Study of Benzene Diffusion in Silicalite-1 through Stochastic Simulations

65 σ.Ο σκοπός της συγκεκριμένης διπλωματικής εργασίας είναι η μελέτη του συστήματος Σιλικαλίτη-1-Βενζολίου με μια στοχαστική προσομοίωση ,την Kinetic Monte Carlo, που θα οδηγήσει στο υπολογισμό του τανυστή διάχυσης D στους 300K.Η προσομοίωση KMC είναι ένας τυχαίος περίπατος σε ένα τρισδιάστατο πλέγμα καταστάσεων που έχει προκύψει από τη Θεωρία Μεταβατικής Κατάστασης.Ο ρυθμός μετάβασης μεταξύ αυτών των καταστάσεων από την ίδια θεωρία η οποία και περιγράφεται. Το βενζόλιο προσομοιώθηκε σαν τυχαίος περιπατητής που κινέιται στο πλέγμα και η κίνησή του καθορίζεται από τις σταθερές ρυθμού μετάβασης.Ο αλγόριθμος KMC βασίζεται στην θεώρηση ότι όλο το φαινόμενο της διάχυσης διέπεται από μια σταχαστική ανέλιξη Poisson.Οι τροχιές των μορίων δεν προκύπτουν ντετερμινιστικά αλλά στοχαστικά. Οι προσομοιώσεις αυτές έγιναν για διάφορες σταθερές ρυθμού που έχουν υπολογιστεί για διαφορετικά ατομιστικά μοντέλα Σιλικαλίτη-1.Τέλος καταλήγουμε σε συμπεράσματα για τη διάχυση και την μέθοδο που χρησιμοποιήθηκε.The scope of this thesis is to study the system Silicallite-1-Benzene through a stochastic simulation, the Kinetic Monte Carlo, and as to calculate the diffusion tensor at 300K. The KMC simulation is a random walk in a three dimensional lattice of states which have been estimated by the Transition State Theory. The transition rates between these states have arisen from the same theory. Benzene is modeled as a random walker moving on the grid and the movement is determined by the transition rate constants. The KMC algorithm is based on the premise that the whole phenomenon of diffusion is governed by a stochastic Poisson process. The trajectories of particles do not result in deterministic but stochastic. The simulations were performed for different rate constants calculated for different atomistic models of Silicallite-1 (flexible or rigid). Finally we arrive at general conclusions on diffusion in Sillicalite-1 and on the method used to calculate the diffusivity.Χριστίνα-Άννα Ι. Γάτσιο

Study of Benzene Diffusion in Silicalite-1 through Stochastic Simulations

65 σ.Ο σκοπός της συγκεκριμένης διπλωματικής εργασίας είναι η μελέτη του συστήματος Σιλικαλίτη-1-Βενζολίου με μια στοχαστική προσομοίωση ,την Kinetic Monte Carlo, που θα οδηγήσει στο υπολογισμό του τανυστή διάχυσης D στους 300K.Η προσομοίωση KMC είναι ένας τυχαίος περίπατος σε ένα τρισδιάστατο πλέγμα καταστάσεων που έχει προκύψει από τη Θεωρία Μεταβατικής Κατάστασης.Ο ρυθμός μετάβασης μεταξύ αυτών των καταστάσεων από την ίδια θεωρία η οποία και περιγράφεται. Το βενζόλιο προσομοιώθηκε σαν τυχαίος περιπατητής που κινέιται στο πλέγμα και η κίνησή του καθορίζεται από τις σταθερές ρυθμού μετάβασης.Ο αλγόριθμος KMC βασίζεται στην θεώρηση ότι όλο το φαινόμενο της διάχυσης διέπεται από μια σταχαστική ανέλιξη Poisson.Οι τροχιές των μορίων δεν προκύπτουν ντετερμινιστικά αλλά στοχαστικά. Οι προσομοιώσεις αυτές έγιναν για διάφορες σταθερές ρυθμού που έχουν υπολογιστεί για διαφορετικά ατομιστικά μοντέλα Σιλικαλίτη-1.Τέλος καταλήγουμε σε συμπεράσματα για τη διάχυση και την μέθοδο που χρησιμοποιήθηκε.The scope of this thesis is to study the system Silicallite-1-Benzene through a stochastic simulation, the Kinetic Monte Carlo, and as to calculate the diffusion tensor at 300K. The KMC simulation is a random walk in a three dimensional lattice of states which have been estimated by the Transition State Theory. The transition rates between these states have arisen from the same theory. Benzene is modeled as a random walker moving on the grid and the movement is determined by the transition rate constants. The KMC algorithm is based on the premise that the whole phenomenon of diffusion is governed by a stochastic Poisson process. The trajectories of particles do not result in deterministic but stochastic. The simulations were performed for different rate constants calculated for different atomistic models of Silicallite-1 (flexible or rigid). Finally we arrive at general conclusions on diffusion in Sillicalite-1 and on the method used to calculate the diffusivity.Χριστίνα-Άννα Ι. Γάτσιο

Optical to Planar X-ray Mouse Image Mapping in Preclinical Nuclear Medicine Using Conditional Adversarial Networks

In the current work, a pix2pix conditional generative adversarial network has been evaluated as a potential solution for generating adequately accurate synthesized morphological X-ray images by translating standard photographic images of mice. Such an approach will benefit 2D functional molecular imaging techniques, such as planar radioisotope and/or fluorescence/bioluminescence imaging, by providing high-resolution information for anatomical mapping, but not for diagnosis, using conventional photographic sensors. Planar functional imaging offers an efficient alternative to biodistribution ex vivo studies and/or 3D high-end molecular imaging systems since it can be effectively used to track new tracers and study the accumulation from zero point in time post-injection. The superimposition of functional information with an artificially produced X-ray image may enhance overall image information in such systems without added complexity and cost. The network has been trained in 700 input (photography)/ground truth (X-ray) paired mouse images and evaluated using a test dataset composed of 80 photographic images and 80 ground truth X-ray images. Performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and Fréchet inception distance (FID) were used to quantitatively evaluate the proposed approach in the acquired dataset

Frequency of family meals and food consumption in families at high risk of type 2 diabetes : the Feel4Diabetes-study

A family meal is defined as a meal consumed together by the members of a family or by having> 1 parent present during a meal. The frequency of family meals has been associated with healthier food intake patterns in both children and parents. This study aimed to investigate in families at high risk for developing type 2 diabetes across Europe the association (i) between family meals' frequency and food consumption and diet quality among parents and (ii) between family meals' frequency and children's food consumption. Moreover, the study aimed to elucidate the mediating effect of parental diet quality on the association between family meals' frequency and children's food consumption. Food consumption frequency and anthropometric were collected cross-sectionally from a representative sample of 1964 families from the European Feel4Diabetes-study. Regression and mediation analyses were applied by gender of children. Positive and significant associations were found between the frequency of family meals and parental food consumption (beta = 0.84; 95% CI 0.57, 1.45) and diet quality (beta = 0.30; 95% CI 0.19, 0.42). For children, more frequent family meals were significantly associated with healthier food consumption (boys, beta = 0.172, p < 0.05; girls, beta = 0.114, p< 0.01). A partial mediation effect of the parental diet quality was shown on the association between the frequency of family meals and the consumption of some selected food items (i.e., milk products and salty snacks) among boys and girls. The strongest mediation effect of parental diet quality was found on the association between the frequency of family breakfast and the consumption of salty snacks and milk and milk products (62.5% and 37.5%, respectively) among girls. Conclusions: The frequency of family meals is positively associated with improved food consumption patterns (i.e., higher intake of fruits and vegetables and reduced consumption of sweets) in both parents and children. However, the association in children is partially mediated by parents' diet quality. The promotion of consuming meals together in the family could be a potentially effective strategy for interventions aiming to establish and maintain healthy food consumption patterns among children