Calculation of the free energy of crystalline solids

The prediction of the packing of molecules into crystalline phases is a key step in understanding the properties of solids. Of particular interest is the phenomenon of polymorphism, which refers to the ability of one compound to form crystals with different structures, which have identical chemical properties, but whose physical properties may vary tremendously. Consequently the control of the polymorphic behavior of a compound is of scientific interest and also of immense industrial importance. Over the last decades there has been growing interest in the development of crystal structure prediction algorithms as a complement and guide to experimental screenings for polymorphs. The majority of existing crystal structure prediction methodologies is based on the minimization of the static lattice energy. Building on recent advances, such approaches have proved increasingly successful in identifying experimentally observed crystals of organic compounds. However, they do not always predict satisfactorily the relative stability among the many predicted structures they generate. This can partly be attributed to the fact that temperature effects are not accounted for in static calculations. Furthermore, existing approaches are not applicable to enantiotropic crystals, in which relative stability is a function of temperature. In this thesis, a method for the calculation of the free energy of crystals is developed with the aim to address these issues. To ensure reliable predictions, it is essential to adopt highly accurate molecular models and to carry out an exhaustive search for putative structures. In view of these requirements, the harmonic approximation in lattice dynamics offers a good balance between accuracy and efficiency. In the models adopted, the intra-molecular interactions are calculated using quantum mechanical techniques; the electrostatic inter-molecular interactions are modeled using an ab-initio derived multipole expansion; a semi-empirical potential is used for the repulsion/dispersion interactions. Rapidly convergent expressions for the calculation of the conditionally and poorly convergent series that arise in the electrostatic model are derived based on the Ewald summation method. Using the proposed approach, the phonon frequencies of argon are predicted successfully using a simple model. With a more detailed model, the effects of temperature on the predicted lattice energy landscapes of imidazole and tetracyanoethylene are investigated. The experimental structure of imidazole is Abstract | ii correctly predicted to be the most stable structure up to the melting point. The phase transition that has been reported between the two known polymorphs of tetracyanoethylene is also observed computationally. Furthermore, the predicted phonon frequencies of the monoclinic form of tetracyanoethylene are in good agreement with experimental data. The potential to extend the approach to predict the effect of temperature on crystal structure by minimizing the free energy is also investigated in the case of argon, with very encouraging results.Open Acces

Transport Properties of Shale Gas in Relation to Kerogen Porosity

Kerogen is a micro-porous amorphous solid, which consist the major component of the organic matter scattered in the potentially lucrative shale formations hosting shale gas. Deeper understanding of the way kerogen porosity characteristics affect the transport properties of hosted gas is important for the optimal design of the extraction process. In this work, we employ molecular simulation techniques in order to investigate the role of porosity on the adsorption and transport behavior of shale gas in overmature type II kerogen found at many currently productive shales. To account for the wide range of porosity characteristics present in the real system, a large set of 60 kerogen structures that exhibit a diverse set of void space attributes was used. Grand Canonical Monte Carlo (GCMC) simulations were performed for the study of the adsorption of CH4, C2H6, n-C4H10 and CO2 at 298.15 K and 398.15 K and a variety of 2 pressures. The amount adsorbed is found to correlate linearly with the porosity of the kerogen. Furthermore, the adsorption of a quaternary mixture of CH4, C2H6, CO2 and N2 was investigated in the same conditions, indicating that the composition resembling that of the shale gas is achieved under higher temperature and pressure values, i.e. conditions closer to these prevailing in the hosting shale field. The diffusion of CH4, C2H6 and CO2, both as pure components and as components of the quaternary mixture, was investigated using equilibrium Molecular Dynamics (MD) simulations at temperatures of 298.15 and 398.15 K and pressures of 1 and 250 atm. In addition to the effect of temperature and pressure, the importance of limiting pore diameter (LPD), maximum pore diameter (MPD), accessible volume (Vacc) and accessible surface (Sacc) on the observed adsorbed amount and diffusion coefficient was revealed by qualitative relationships. The diffusion across the models was found to be anisotropic and the maximum component of the diffusion coefficient to correlate linearly with LPD, indicating that the controlling step of the transport process is the crossing of the limiting pore region. Finally, the transport behavior of the pure compounds was compared with their transport properties when in mixture and it was found that the diffusion coefficient of each compound in the mixture is similar to the corresponding one in pure. This observation agrees with earlier studies in different kerogen models comprising wider pores that have revealed negligible cross-correlation Onsager coefficients

Modeling of bulk kerogen porosity: Methods for control and characterization

Shale gas is an unconventional source of energy, which has attracted a lot of attention during the last years. Kerogen is a prime constituent of shale formations and plays a crucial role in shale gas technology. Significant experimental effort in the study of shales and kerogen has produced a broad diversity of experimentally determined structural and thermodynamic properties even for samples of the same well. Moreover, proposed methods reported in the literature for constructing realistic bulk kerogen configurations have not been thoroughly investigated. One of the most important characteristics of kerogens is their porosity, due to its direct connection with their transport properties and its potential as discriminating and classifying metric between samples. In this study, molecular dynamics (MD) simulations are used to study the porosity of model kerogens. The porosity is controlled effectively with systematic variations of the number and the size of dummy LJ particles that are used during the construction of system’s configuration. The porosity of each sample is characterized with a newly proposed algorithm for analyzing the free space of amorphous materials. It is found that, with moderately sized configurations, it is possible to construct percolated pores of interest in the shale gas industry

Transport Properties of Shale Gas in Relation to Kerogen Porosity

Kerogen is a micro-porous amorphous solid, which consist the major component of the organic matter scattered in the potentially lucrative shale formations hosting shale gas. Deeper understanding of the way kerogen porosity characteristics affect the transport properties of hosted gas is important for the optimal design of the extraction process. In this work, we employ molecular simulation techniques in order to investigate the role of porosity on the adsorption and transport behavior of shale gas in overmature type II kerogen found at many currently productive shales. To account for the wide range of porosity characteristics present in the real system, a large set of 60 kerogen structures that exhibit a diverse set of void space attributes was used. Grand Canonical Monte Carlo (GCMC) simulations were performed for the study of the adsorption of CH4, C2H6, n-C4H10 and CO2 at 298.15 K and 398.15 K and a variety of 2 pressures. The amount adsorbed is found to correlate linearly with the porosity of the kerogen. Furthermore, the adsorption of a quaternary mixture of CH4, C2H6, CO2 and N2 was investigated in the same conditions, indicating that the composition resembling that of the shale gas is achieved under higher temperature and pressure values, i.e. conditions closer to these prevailing in the hosting shale field. The diffusion of CH4, C2H6 and CO2, both as pure components and as components of the quaternary mixture, was investigated using equilibrium Molecular Dynamics (MD) simulations at temperatures of 298.15 and 398.15 K and pressures of 1 and 250 atm. In addition to the effect of temperature and pressure, the importance of limiting pore diameter (LPD), maximum pore diameter (MPD), accessible volume (Vacc) and accessible surface (Sacc) on the observed adsorbed amount and diffusion coefficient was revealed by qualitative relationships. The diffusion across the models was found to be anisotropic and the maximum component of the diffusion coefficient to correlate linearly with LPD, indicating that the controlling step of the transport process is the crossing of the limiting pore region. Finally, the transport behavior of the pure compounds was compared with their transport properties when in mixture and it was found that the diffusion coefficient of each compound in the mixture is similar to the corresponding one in pure. This observation agrees with earlier studies in different kerogen models comprising wider pores that have revealed negligible cross-correlation Onsager coefficients

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

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 submissions, 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

Human pose estimation from sparse 3D Data on low power systems

This thesis deals with the investigation of novel techniques for human pose estimation (HPE) using sparse depth/3D data, in order to develop a standalone, high-accuracy, low-latency human pose estimation module, suitable for deployment in systems with limited processing resources. Based on the existing work and motivated by the significant progress that has been achieved in the relevant fields, two novel methods for the estimation and tracking of the human pose utilising sparse depth/3D data, are proposed. First, a real-time human pose estimation and tracking framework is developed, which builds upon an already established human-template-tracking based approach, utilising the 3D Signed Distance Function (SDF) data representation. A series of complementary tracking features are introduced, tackling specifically the issues of free space violation, body part visibility and leg intersection, which are typically encountered under real-life monitoring conditions. The method is experimentally evaluated on a series of publicly available datasets, achieving state-of-the-art (SOA) performance, while also successfully utilised for human behavioural modelling on an autonomous robotic platform. Due to inherent limitations of this tracking-based approach, such as the requirement for clearly segmented human/background data and the use of an out-of-the-box initialiser, a second, deep learning-based architecture is investigated. Specifically, a detection-based 3D-CNN architecture for 3D human pose estimation from 3D data is introduced, following the sequential network architecture paradigm. It utilises a volumetric data representation, and generates 3D heatmaps corresponding to potential locations of the human joints in the scene, achieving state-of-the-art accuracy. Additionally, a 3D body-part detector is incorporated, extending the architecture towards multi-person 3D pose estimation, the first such method for 3D data. However, the 3D CNN architecture comes at a steep computational cost, making it unsuitable for implementation on low power systems. Thus, the final contribution of this thesis includes the investigation of computationally efficient 3D CNN design guidelines, in order to reduce the computational complexity of the developed model. The result of this investigation is a novel 3D-CNN architecture for multi-person pose estimation from 3D data, composed mainly of 3D depthwise residual bottleneck units, SE blocks and a decomposed strided input layer. This optimised version performs comparably to SOA methods on two public datasets, while requiring significantly fewer computational resources and achieving a speedup of over 100x on a modern low power mobile device, and a reduction in model size of approximately 50x.Open Acces

Transport Properties of Shale Gas in Relation to Kerogen Porosity

Kerogen is a microporous amorphous solid, which is the major component of the organic matter scattered in the potentially lucrative shale formations hosting shale gas. A deeper understanding of the way kerogen porosity characteristics affect the transport properties of hosted gas is important for the optimal design of the extraction process. In this work, we employ molecular simulation techniques to investigate the role of porosity on the adsorption and transport behavior of shale gas in overmature type II kerogen found in many currently productive shales. To account for the wide range of porosity characteristics present in the real system, a large set of 60 kerogen structures that exhibit a diverse set of void space attributes was used. Grand canonical Monte Carlo simulations were performed for the study of the adsorption of CH₄, C₂H₆, <i>n-</i>C₄H₁₀, and CO₂ at 298.15 and 398.15 K and a variety of pressures. The amount adsorbed is found to correlate linearly with the porosity of the kerogen. Furthermore, the adsorption of a quaternary mixture of CH₄, C₂H₆, CO₂, and N₂ was investigated under the same conditions, indicating that a composition resembling that of the shale gas is achieved under higher temperature and pressure values, i.e., conditions closer to those prevailing in the hosting shale field. The diffusion of CH₄, C₂H₆, and CO₂, both as pure components and as components of the quaternary mixture, was investigated using equilibrium molecular dynamics simulations at temperatures of 298.15 and 398.15 K and pressures of 1 and 250 atm. In addition to the effect of temperature and pressure, the importance of limiting pore diameter (LPD), maximum pore diameter (MPD), accessible volume (<i>V</i>_acc), and accessible surface (<i>S</i>_acc) on the observed adsorbed amount and diffusion coefficient was revealed by qualitative relationships. The diffusion across the models was found to be anisotropic and the maximum component of the diffusion coefficient to correlate linearly with LPD, indicating that the controlling step of the transport process is the crossing of the limiting pore region. Finally, the transport behavior of the pure compounds was compared with their transport properties when in mixture and it was found that the diffusion coefficient of each compound in the mixture is similar to the corresponding one under pure conditions. This observation agrees with earlier studies in different kerogen models comprising wider pores that have revealed negligible cross-correlation Onsager coefficients

Dietary mastic oil extracted from Pistacia lentiscus var. chia suppresses tumor growth in experimental colon cancer models

Plant-derived bioactive compounds attract considerable interest as potential chemopreventive anticancer agents. We analyzed the volatile dietary phytochemicals (terpenes) present in mastic oil extracted from the resin of Pistacia lentiscus var. chia and comparatively investigated their effects on colon carcinoma proliferation, a) in vitro against colon cancer cell lines and b) in vivo on tumor growth in mice following oral administration. Mastic oil inhibited - more effectively than its major constituentsproliferation of colon cancer cells in vitro, attenuated migration and downregulated transcriptional expression of survivin (BIRC5a). When administered orally, mastic oil inhibited the growth of colon carcinoma tumors in mice. A reduced expression of Ki-67 and survivin in tumor tissues accompanied the observed effects. Notably, only mastic oil -which is comprised of 67.7% α-pinene and 18.8% myrceneinduced a statistically significant anti-tumor effect in mice but not α-pinene, myrcene or a combination thereof. Thus, mastic oil, as a combination of terpenes, exerts growth inhibitory effects against colon carcinoma, suggesting a nutraceutical potential in the fight against colon cancer. To our knowledge, this is the first report showing that orally administered mastic oil induces tumor-suppressing effects against experimental colon cancer

Linear-complexity relaxed word Mover's distance with GPU acceleration

The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents. Whereas low-complexity similarity metrics are available, attention has been shifting towards more complex methods that achieve a higher accuracy. In particular, the Word Mover's Distance (WMD) method proposed by Kusner et al. is a promising new approach, but its time complexity grows cubically with the number of unique words in the documents. The Relaxed Word Mover's Distance (RWMD) method, again proposed by Kusner et al., reduces the time complexity from qubic to quadratic and results in a limited loss in accuracy compared with WMD. Our work contributes a low-complexity implementation of the RWMD that reduces the average time complexity to linear when operating on large sets of documents. Our linear-complexity RWMD implementation, henceforth referred to as LC-RWMD, maps well onto GPUs and can be efficiently distributed across a cluster of GPUs. Our experiments on real-life datasets demonstrate 1) a performance improvement of two orders of magnitude with respect to our GPU-based distributed implementation of the quadratic RWMD, and 2) a performance improvement of three to four orders of magnitude with respect to our distributed WMD implementation that uses GPU-based RWMD for pruning.Comment: To appear in the 2017 IEEE International Conference on Big Data (Big Data 2017) http://cci.drexel.edu/bigdata/bigdata2017/ December 11-14, 2017, Boston, MA, US