Ponnadurai Ramasami (Ed.) Computational Sciences Also of interest Computational Strong-Field Quantum Dynamics Intense Light-Matter Interactions Bauer (Ed.), 2017 ISBN 978-3-11-041725-8, e-ISBN 978-3-11-041726-5 Optimal Structural Design Contact Problems and High-Speed Penetration Banichuk, Ivanova, 2017 ISBN 978-3-11-053080-3, e-ISBN 978-3-11-053118-3 Computational Physics With Worked Out Examples in FORTRAN and MATLAB Bestehorn, 2018 ISBN 978-3-11-051513-8, e-ISBN (PDF) 978-3-11-051514-5 Multiscale Materials Modeling Approaches to Full Multiscaling Schmauder, Schäfer (Eds.), 2016 ISBN 978-3-11-041236-9, e-ISBN (PDF) 978-3-11-041245-1 Computational Sciences Edited by Ponnadurai Ramasami Editor Prof Ponnadurai Ramasami University of Mauritius Department of Chemistry Réduit 80837, Mauritius p.ramasami@uom.ac.mu ISBN 978-3-11-046536-5 e-ISBN (PDF) 978-3-11-046721-5 e-ISBN (EPUB) 978-3-11-046548-8 The articles in this book have been previously published in the journal Physical Sciences Reviews (ISSN 2365–659X) Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de © 2017 Walter de Gruyter GmbH, Berlin/Boston Cover image: KTSDESIGN/Science Photo Library/getty images Typesetting: Integra Software Services Pvt Ltd Printing and binding: CPI books GmbH, Leck ♾ Printed on acid-free paper Printed in Germany www.degruyter.com Preface of the Book of Proceedings of the Virtual Conference on Computational Science (VCCS-2016) A virtual conference on computational science (VCCS-2016) was organized online from 1st to 31st August 2016 This was the fourth virtual conference which was started in 2013 The month of August was chosen to commemorate the birth anniversary of Erwin Schrödinger, the father of quantum mechanics, on 12th August There were 30 presentations for the virtual conference with 100 participants from 20 countries A secured platform was used for virtual interactions of the participants After the virtual conference, there was a call for full papers to be considered for publication in the conference proceedings Manuscripts were received and they were processed and reviewed as per the policy of De Gruyter This book is a collection of the eleven accepted manuscripts These manuscripts cover a range of topics from fundamental to applied science using computational methods Marcano investigated anthocyanidin and anthocyanin pigments for dye sensitized solar cells based on Density Functional Theory (DFT) method DFT method was also used by Ranjan et al to study silver-gold nanoclusters Gbayo et al applied DFT method to understand the mechanism of nucleophilic substitution reactions of 4-(4’-nitro)phenylnitrobenzofurazan ether with aniline in acetonitrile Khairat et al used high level ab initio method to probe the electronic states of two newly detected dications namely MgS2+ and SiN2+ Palafox applied several ab initio methods to vibrational spectroscopy for a better characterization and assignment of all the bands of the experimental spectra Majee and Biswas used computational algorithm methods where Spatial Aggregation Propensity was employed and molecular dynamics simulation approach for prediction of aggregation prone areas in monoclonal antibodies Chowdhury et al studied the optical and magnetic properties of free standing two-dimensional (2D) materials silicene, germanene and T-graphene while the contribution by Schwingenschlögl et al provides a review of elemental two-dimensional materials beyond graphene Ocaya and Terblans addressed the challenges of standalone multi-core simulations in molecular dynamics Patade and Bhalekar analyzed pantograph equation with incommensurate delay and provide analytical solution Chooramun et al used the Hybrid Space Discretisation to simulate evacuation and this was applied to large underground rail tunnel station I hope that these chapters will add to literature and they will be useful references To conclude, VCCS-2016 was a successful event and I would like to thank all those who have contributed I would also like to thank the Organising and International Advisory committee members, the participants and the reviewers We are currently planning for the VCCS-2017 to be held from 1st to 31st August 2017 https://doi.org/10.1515/9783110467215-202 VI Preface Prof Ponnadurai Ramasami Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: p.ramasami@uom.ac.mu Contents Preface V List of contributing authors XI R.O Ocaya and J.J Terblans Addressing the challenges of standalone multi-core simulations in molecular dynamics 1.1 Introduction 1.2 Standalone architectures 1.2.1 Classifications of parallelization paradigms 1.3 Getting started with standalone computation 1.3.1 To code or not to code 1.3.2 General tools 1.3.3 Parallelizable tools 1.4 Parallel processing paradigms in the C-language 10 1.4.1 Threads and message passing 10 1.4.2 Open multiprocessing programming 11 1.4.3 Message passing interface programming 13 1.4.4 The GPU approach 14 1.4.5 Cloud virtualization 15 1.5 Summary of results 17 1.6 Conclusions 17 References 18 Suman Chowdhury, Arka Bandyopadhyay, Namrata Dhar and Debnarayan Jana Optical and magnetic properties of free-standing silicene, germanene and T-graphene system 23 2.1 Introduction 23 2.2 DFT study of the optical properties 27 2.2.1 Methodology 27 2.3 FS silicene monolayer 30 2.3.1 Optical properties 30 2.3.2 Magnetic properties of doped FS silicene monolayer 36 2.4 Elemental structure and synthesis of FS germanene 44 2.4.1 Electronic and magnetic properties of FS germanene 45 2.4.2 Optical properties of FS germanene 50 2.5 Structural properties of TG sheet 51 2.5.1 Electronic properties of pristine and functionalized TG sheet 53 2.5.2 TG nanoribbons (NRs) and clusters 54 2.5.3 Other allotropes beyond TG 57 VIII 2.6 Contents Conclusions and future directions References 61 59 Toufik Khairat, Mohammed Salah, Khadija Marakchi and Najia Komiha Theoretical study of the electronic states of newly detected dications 71 Case of MgS2+ AND SiN2+ 3.1 Introduction 71 3.2 Computational details 72 3.3 Results and discussion 76 3.3.1 Neutral MgS 76 79 3.3.2 MgS2+ dication 83 3.3.3 SiN2+ dication study 3.4 Conclusion 88 References 90 Jayvant Patade and Sachin Bhalekar Analytical Solution of Pantograph Equation with Incommensurate Delay 93 4.1 Introduction 93 4.2 Preliminaries 94 4.2.1 Basic definitions and results 94 4.2.2 Daftardar-Gejji and Jafari method 96 4.2.3 Existence, uniqueness and convergence 98 4.3 Stability analysis 101 4.4 The pantograph equation and its solution 102 4.5 Analysis 103 4.5.1 The relation between Rða, b, c, p, q, xÞ and Kummer’s confluent hypergeometric function 108 4.6 Generalizations to fractional-order DDE 113 4.7 Conclusions 114 References 114 M Alcolea Palafox Computational chemistry applied to vibrational spectroscopy: A tool for characterization of nucleic acid bases and some of their 5-substituted derivatives 117 5.1 Introduction 117 5.2 Molecules under study 118 5.3 Computational methods 120 5.4 Scaling 122 5.5 Applications of computational chemistry to vibrational spectroscopy 123 Contents 5.5.1 5.5.2 5.5.3 5.5.4 5.6 Characterization of all the normal modes of a molecule 123 Accurate assignment of all the bands of a spectrum 123 Identification of the tautomers present in the isolated state 135 Simulation of the crystal unit cell of a compound and the interpretation of its vibrational spectra 136 Summary and conclusions 147 References 149 K Gbayo, C Isanbor, K Lobb and O Oloba-Whenu Mechanism of nucleophilic substitution reactions of 4-(4’-nitro) phenylnitrobenzofurazan ether with aniline in acetonitrile 153 6.1 Introduction 153 6.2 Results and discussion 154 6.3 Conclusion 158 6.4 Experimental section 159 References 160 Sutapa Biswas Majee and Gopa Roy Biswas Computational methods in preformulation study for pharmaceutical solid dosage forms of therapeutic proteins 163 7.1 Introduction 163 7.2 Challenges to formulation development of therapeutic proteins 164 7.3 Aggregation of therapeutic proteins 165 7.3.1 Instrumental methods of analysis 166 7.3.2 Computational approaches in study of aggregation 169 7.4 Computational tools in assessment of immunogenicity of therapeutic proteins 170 7.5 Conclusion 170 References 171 Prabhat Ranjan, Tanmoy Chakraborty and Ajay Kumar Computational Investigation of Cationic, Anionic and Neutral Ag2AuN (N = 1–7) Nanoalloy Clusters 173 8.1 Introduction 173 8.2 Computational details 175 8.3 Results and discussion 176 8.3.1 Equilibrium geometries 176 8.3.2 Electronic properties and DFT-based descriptors 180 8.4 Conclusion 185 References 185 IX 11.2 Microscopic mechanism of the oxidation of silicene on Ag(111) 221 field is induced by charge transfer, which breaks the symmetry of the two silicene sublattices [9] The band gap is also sensitive to lateral shifts between silicene and substrate, as demonstrated on WSe2 in Ref [5], ranging from to 320 meV Te and Se doping can make the system semiconducting and metallic, respectively The possibility to achieve large band gaps is promising for ultra-high speed (THz frequency range) field-effect transistors with high on/off current ratio 11.2 Microscopic mechanism of the oxidation of silicene on Ag(111) Silicene has the great advantage of easy integration into existing circuitry that is already based on Si technology The application of silicene to nanoscale devices is, however, currently hindered by the existence of unsaturated (dangling) bonds on its surface, which makes it highly reactive under atmospheric conditions It is, however, crucial to be able to control the stability of silicene under atmospheric conditions in order to fabricate silicene-based nanodevices In particular, elucidating the reaction process of silicene with oxygen is urgently required We here review recent ab initio molecular-dynamics (AIMD) simulations to uncover the atomistic mechanism of the oxidation process of the silicene overlayer on the Ag(111) surface [10] The AIMD simulations were performed for the 3×3 honeycomb silicene lattice on the 4×4 Ag(111) surface within the framework of density functional theory as implemented in the Vienna Ab Initio Simulation Package The exchange-correlation functional in the Perdew–Burke–Ernzerhof form was used and the ion-electron interaction was described by the projector augmented wave method The electronic wave functions were expanded in a plane-wave basis set with an energy cutoff of 400 eV The Ag substrate consists of five atomic layers with the bottom layer fixed It was found that there exist barrier-less oxygen chemisorption pathways around the outer Si atoms of the silicene overlayer, indicating that oxygen can easily react with a Si atom to form an Si-O-Si configuration, once the molecule finds an entrance to the pathway on the rugged energy landscape provided by the silicene overlayer The Si-O bond formed in the reaction of oxygen is not covalent but rather ionic, which results from the charge transfer from the Si atom to the O atom It was found that about 0.8|e| is transferred from Si to O atoms As a result, the nature of the intermediate sp2/sp3 bonding in the silicene overlayer is substantially degraded upon oxidation In the reaction process involving multiple O2 molecules, a synergistic effect between the molecular dissociation and subsequent structural rearrangements was found to accelerate the oxidation process, especially at a high oxygen dose This effect enhances self-organized formation of sp3-like tetrahedral configurations (consisting of Si and O atoms), which results in collapse of the two-dimensional silicene structure and its exfoliation from the substrate (see Figure 11.2) [10] It was also found that the electronic properties of silicene can be significantly altered by oxidation Figure 11.2 shows the atom-resolved density of states (DOS) for a 222 11 Elemental Two-Dimensional Materials Beyond Graphene s Px Py Pz DOS (arb units) (b) s Px Py Pz DOS (arb units) (a) -4 -4 -2 Energy (eV) -2 Energy (eV) 4 Figure 11.2: Decomposed electronic DOS for (a) a threefold-coordinated Si atom and (b) a fourfoldcoordinated Si atom in the oxidized silicene (as indicated by the bold arrows and white circles) The zero of energy in the DOS plots is aligned to the Fermi energy The red, yellow, and pink balls indicate O, Si, and Ag atoms, respectively [10] Si atom having three neighboring Si atoms, as in the silicene honeycomb lattice It is clear that the electronic bands near the Fermi energy have a high intensity and are dominated by the pz electrons from the dangling bond on the Si atom, showing a metallic nature In contrast, the atom-resolved DOS for a four-coordinated Si atom (with two O atoms and two Si atoms), having a highly tetrahedral configuration [Figure 11.2], shows completely different characteristics with the electronic bands near the Fermi energy being substantially reduced, especially those from the pz electrons, due to capping of the dangling bonds with O atoms We thus conclude that the metallic nature of silicene is reduced as oxidation proceeds This tendency has also been observed in a recent experimental study [11] 11.3 Multilayer silicene Recent work on multilayer silicene using an energy-dispersive grazing incidence x-ray diffraction (ED-GIXRD) study is now summarized [12] The growth of multilayer silicene has been realized at a temperature of about 200 °C on top of the initial archetype 3×3 monolayer silicene phase on a single crystal Ag(111) surface It proceeds in successive flat terraces separated by 0.3 nm These terraces show a unique √3×√3 reconstruction whose cell size was found to be 6.477 ± 0.015 Å by ED-GIXRD, in agreement with scanning tunneling microscopy measurements Figure 11.3 displays the ED-GIXRD pattern from 10 monolayer (ML) thick multilayer silicene The first- and second-order in-plane reflections at qxy = 0.970 ± 0.005 Å–1 and qxy = 1.939 ± 0.005 Å–1, corresponding to the √3×√3 cell size of multilayer silicene, aML = 6.477 ± 0.015 Å, and, in addition, the out-of-plane reflection at qz = 2.033 ± 0.005 Å–1, corresponding to dzML = 3.090 ± 0.010 Å, as previously reported, are clearly recorded Particularly noteworthy is that absolutely no Si(220) reflection was detected (see inset on the right side of Figure 11.3) This proves that the whole body of the multilayer Diffracted Intensity (arb units) 11.4 Germanene 223 10 MLs √3×√3 film prepared at ≈200°C qxy = 0.970 Å-1 aML = 6.477 Å 0.90 0.93 0.96 0.99 3.00 3.25 qx,y (Å-1) qxy = 1.939 Å-1 1.9 3.50 qz = 2.033 Å-1 dz = 3.090 Å 2.0 2.1 scattering parameter (Å-1) Figure 11.3: ED-GIXRD pattern collected from a √3×√3 multilayer film (10 MLs) grown on Ag(111) at 200 °C and Gaussian fit (red line) of each reflection First-order in-plane qxy = 0.970 Å–1 (FWHMxy = 0.0467 Å–1) and out-of-plane qz = 2.033 Å–1 (FWHMz = 0.2090 Å–1) reflections; second-order inplane reflection qxy = 1.939 Å–1 (FWHMxy = 0.0790 Å–1) (blue line) The inset displays the ED-GIXRD pattern around 3.27 Å–1; neither a Si(220) peak nor its relaxation are observed The positions of the peaks are the centroid of the Gaussians Adapted from Figure 11.3 of Ref [12] silicene film possesses the aML = 6.477 ± 0.015 Å in-plane lattice parameter, totally different from: i) a bulk-like Si(111) arrangement terminated by a √3×√3-Ag reconstruction (aSi(111)√3 = 6.655 ± 0.015 Å), ii) a tetragonally strained bulk-like Si(111) arrangement terminated by a Si(111)-√3×√3-Ag reconstruction; and finally iii) a bulk-like Si(111) arrangement terminated by a Si(111)-√3×√3 intrinsic reconstruction These measurements [12] have demonstrated the existence of multilayer silicene in the low temperature growth regime 11.4 Germanene Germanene, the germanium analogue of graphene, has been successfully synthesized in 2014 by three different groups: in July 2014 by the Gao group [13] and in September 2014 by the Le Lay [14] and Zandvliet [15] groups The structural and electronic properties of germanene and silicene are predicted to be very similar to graphene [16, 17] However, there are also a few distinct differences, such as the honeycomb lattice, which is fully planar for graphene, but buckled for silicene and germanene [18] Despite this buckling, tight binding and density functional theory calculations have revealed that the Dirac properties of silicene and germanene are not destroyed Near the K and K′ points of the Brillouin zone the energy bands of silicene and germanene are 224 11 Elemental Two-Dimensional Materials Beyond Graphene predicted to be linear in k Besides this buckling of the honeycomb lattice, there is another very salient difference between graphene and silicene/germanene, namely the size of the spin-orbit gap The spin-orbit gap in graphene, silicene, and germanene makes that these materials are not true semimetals but rather two-dimensional topological insulators The spin-orbit gap in graphene is only 24 μeV [19], whereas the spin-orbit gaps in silicene and germanene are substantially larger, namely 1.55 meV and 23.9 meV [20], respectively Silicene and particularly germanene are thus ideal candidates to exhibit the quantum spin Hall effect at experimentally accessible temperatures In Figure 11.4 a scanning tunneling microscopy image of germanene recorded by Bampoulis et al [15] is shown The germanene layer was synthesized on a Ge2Pt crystal Bampoulis et al [15] managed to resolve the atomic structure of the unit cell of germanene As can be seen in Figure 11.4, the honeycomb cell of germanene is buckled, i e., half of the atoms reside in a ‘high’ position, whereas the other half of the atoms reside in a ‘low’ position The buckling is only 0.2 Å, i e., substantially smaller than the buckling predicted for freestanding germanene (0.65 Å) [17] So far germanene has been grown only on metallic substrates [13–15, 18, 21–24] Unfortunately, metallic substrates are often detrimental for the Dirac nature of the two-dimensional materials, because the relevant electronic states near the Fermi Figure 11.4: Scanning tunneling microscopy image recorded at a sample bias of 0.5 V and a tunnel current of 0.2 nA The image size is 4.5 nm × 4.5 nm The nearest neighbor distance between the Ge atoms is 2.5 ± 0.1 Å and the buckling is 0.2 Å Data taken from Ref [15] 225 dl/dV [a.u.] 11.5 Summary –0.8 –0.4 0.4 0.8 Sample bias [V] Figure 11.5: Differential conductivities of a germanene layer synthesized on a molybdenum disulfide substrate (solid line) and of bare molybdenum disulfide (dashed line) The set points are –1.4 V and 0.6 nA Data from Ref [23] level can hybridize with the electronic states of the metallic substrate In 2016, Zhang et al [23] succeeded to grow germanene on molybdenum disulfide, a band gap material They performed scanning tunneling spectroscopy measurements and showed that the DOS of germanene exhibits a well-defined V-shape, which is one of the hallmarks of a two-dimensional Dirac system (see Figure 11.5) The Dirac point is located very close to the Fermi level, but the DOS does not completely vanish at the Dirac point Density functional theory calculations showed that this non-zero DOS at the Fermi level is due to the fact that the buckling of the germanene layer is somewhat larger than the buckling of freestanding germanene For a buckling larger than about 0.8 Å two electronic states, that originate from two parabolic bands of germanene, cross the Fermi level at the Γ point of the surface Brillouin zone The exact effect of this non-zero DOS on the Dirac properties remains to be investigated In any case density functional theory calculations by Amlaki et al [24] have revealed that the topological insulator character of germanene remains intact upon the interaction with molybdenum disulfide 11.5 Summary We have elaborated on a few properties of monolayer silicene, multilayer silicene, and germanene It has been shown that the Dirac cone of silicene is preserved on some substrates, whereas on other substrates energy gaps are created Silicene oxidation studies show that this process can lead to exfoliation and significant changes of the electronic properties of silicene Energy-dispersive grazing incidence x-ray diffraction has been used to confirm the growth of multilayer silicene in the low 226 11 Elemental Two-Dimensional Materials Beyond Graphene temperature regime Finally, recent work on the synthesis of germanene on metallic as well as semiconducting substrates has been briefly discussed High-resolution scanning tunneling microscopy data reveal that germanene exhibits a buckled honeycomb lattice The DOS of germanene synthesized on various substrates has a V-shape, which strongly hints to a two-dimensional Dirac material References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] Vogt P, De Padova P, Quaresima C, Avila J, Frantzeskakis E, Asensio MC, et al Silicene: Compelling experimental evidence for graphenelike two-dimensional silicon Phys Rev Lett 2012;108:155501 Meng L, Wang Y, Zhang L, Du S, Wu R, Li L, et al Buckled silicene formation on Ir(111) Nano Lett 2013;13:685–690 Fleurence A, Friedlein R, Ozaki T, Kawai H, Wang Y, Yamada-Takamura Y Experimental evidence for epitaxial silicene on diboride thin films Phys Rev Lett 2012;108:245501 Zhu J, Schwingenschlögl U Structural and electronic properties of silicene on MgX2 (X = Cl, Br, and I) ACS Appl Mater Interfaces 2014;6:11675–11681 Zhu J, Schwingenschlögl U Stability and electronic properties of silicene on WSe2 Mater Chem C 2015;3:3946–3953 Zhu J, Schwingenschlögl U Silicene on MoS2: Role of the van der Waals interaction 2D Mater 2015;2:045004 Sattar S, Hoffmann R, Schwingenschlögl U Solid argon as a possible substrate for quasifreestanding silicene New J Phys 2014;16:065001 Chiappe D, Scalise E, Cinquanta E, Grazianetti C, Van Den Broek B, Fanciulli M, et al Twodimensional Si nanosheets with local hexagonal structure on a MoS2 surface Adv Mater 2014;26:2096–2101 Zhu J, Schwingenschlögl U Band gap opening in silicene on MgBr2(0001) induced by Li and Na ACS Appl Mater Interfaces 2014;6:19242–19245 Morishita T, Spencer MJ How silicene on Ag(111) oxidizes: Microscopic mechanism of the reaction of O2 with silicene Sci Rep 2015;5:17570 Du Y, Zhuang J, Liu H, Xu X, Eilers S, Wu K, et al Tuning the band gap in silicene by oxidation ACS Nano 2014;8:10019–10025 De Padova P, Generosi A, Paci B, Ottaviani C, Quaresima C, Olivieri B, et al Multilayer silicene: Clear evidence 2D Mater 2016;3:031011 Li L, Lu S-Z, Pan J, Qin Z, Wang Y-Q, Wang Y, et al Buckled germanene formation on Pt(111) Adv Mat 2014;26:4820–4824 Dávila ME, Xian L, Cahangirov S, Rubio A, Le Lay G Germanene: A novel two-dimensional germanium allotrope akin to graphene and silicene New J Phys 2014;16:095002 Bampoulis P, Zhang L, Safaei A, Van Gastel R, Poelsema B, Zandvliet HJW Germanene termination of Ge2Pt crystals on Ge(110) J Phys Cond Mat 2014;26:442001 Guzmán-Verri GG, Lew Yan Voon LC Electronic structure of silicon-based nanostructures Phys Rev B 2007;76:075131 Cahangirov S, Topsakal M, Aktürk E, Şahin H, Ciraci S Two- and one-dimensional honeycomb structures of silicon and germanium Phys Rev Lett 2009;102:236804 Acun A, Zhang L, Bampoulis P, Farmanbar M, Van Houselt A, Rudenko AN, et al Germanene: The germanium analogue of graphene J Phys Cond Matt 2015;27:443002 Yao Y, Ye F, Qi X-L, Zhang S-C, Fang Z Spin-orbit gap of graphene: First-principles calculations Phys Rev B 2007;75:041401 References 227 [20] Liu CC, Feng W, Yao Y Quantum spin hall effect in silicene and two-dimensional germanium Phys Rev Lett 2011;107:076802 [21] Zhang L, Bampoulis P, Van Houselt A, Zandvliet HJW Two-dimensional Dirac signature of germanene Appl Phys Lett 2015;107:111605 [22] Dávila ME, Le Lay G Few layer epitaxial germanene: A novel two-dimensional Dirac material Sci Rep 2016;6:20714 [23] Zhang L, Bampoulis P, Rudenko AN, Yao Q, Van Houselt A, Poelsema B, et al Structural and electronic properties of germanene on MoS2 Phys Rev Lett 2016;116:256804 [24] Amlaki T, Bokdam M, Kelly PJ Z2 invariance of germanene on MoS2 from first principles Phys Rev Lett 2016;116:256805 Index ab initio molecular-dynamics (AIMD) 221 ab-initio 2, 48, 71, 72, 89, 117, 121, 221 absorption coefficient 29, 31 accelerator 15 accuracy of the ground state 86 accuracy 2, 3, 8, 9, 15, 17, 51, 86, 123, 125, 133–135, 148, 167–171, 175, 192, 202 acetylenic 57, 58 acknowledgements 60, 114 adsorption energies 205–208, 213, 214 Ag(111) 24, 221–222 Ag2AuN Clusters 180 Ag-Au Clusters 174, 180, 181, 183 Agent Avoidance behaviour 197, 202 agent behaviours 193, 198, 202 agent interactions 200 agent movement 192, 193, 195, 196 agent movement in hybrid model 199 agent transitions 202 AGGRESCAN 3D 170 algorithm 3, 5, 6, 8, 9, 10, 15, 166, 169, 170, 196 alkyl 36 All-Coarse 192, 194, 202 all-Continuous 193, 194, 202 All-Fine model 192, 194, 199–202 allotropes 24, 26, 52, 57–60 α-helix 165 amino-oxo 117, 119, 120, 136, 147 anharmonic 73, 77, 80, 82, 85–87, 118, 125 anharmonic frequencies 77, 80 anharmonic terms 77, 80 aniline 153–159 anionic 173–185 anisotropy 30, 33, 35 anthocyanin pigments 205–215 antibody – grafting 170 API 11, 164 application of HSD 196 application of hybrid model to rail tunnel 191–203 architecture 6–8, 10, 14, 193–196 Ar matrix 118, 125, 126, 130, 136 armchair triangular 32 As 49, 50 AV5Z basis set 76 average run time 200, 201 https://doi.org/10.1515/9783110467215-012 average total evacuation time 200 average unit flow rate 201 B3LYP 117, 118, 122–127, 130, 133, 135, 137, 140–142, 145, 205–211, 212 balancing 15 band gaps 205, 220, 221, 225 basis sets 71, 76, 117, 121–123, 125, 126, 130–131, 135, 136, 175, 206, 208, 214, 221 benchmark 4, 8–10 bending 130, 133, 135, 147 benefits of HSD 192, 202 β-sheet 165, 166, 168 Bethe Salpeter 34 bimetallic clusters 174, 175, 180–184 binary compounds 45, 47 binding energy 50, 53, 174, 175 biopharmaceutical 164, 165 blade 14 bond length 44, 51, 55, 86, 89, 135, 145, 180, 183, 213, 214 boron-nitride 25 bound electronic states 77, 80, 88 bound states 72, 76, 78, 79, 80, 89 bowtie 32 bridge-site (BS) 44 buckling 24, 25, 30, 36, 42, 44, 48, 50, 53, 60, 219, 223, 224, 225 buckling height 24, 219 buildingEXODUS 192, 193, 198 buildingEXODUS-Hybrid 193 buildingEXODUS software architecture 192, 193 C4 119, 133, 197, 198, 201 C language 10–17 cache 4, 6, capability of HSD 196, 202 CASSCF 72, 76 cationic clusters 173–185 characteristic/characterizing/characterization 23, 24, 25, 31, 33, 34, 36, 50, 55, 56, 71, 93, 117–148, 164, 166, 167, 168, 174, 198, 206, 208, 222 charge collection efficiency 207 charge transfer 54, 207, 208, 210, 214, 221 chemisorption 213–215, 221 230 Index chemometric – analysis 168 cloud 15–18 cluster(s) 3, 4, 7, 10, 14–17, 53–56, 174–185, 207, 208, 213 Coarse models 192, 196 Coarse region component 195–196 code snippet 13 communication protocols computational chemistry 117–147 computational performance 5, 202 computational power 3, 5, conceptual density functional theory 175 conduction band minimum (CBM) 35 configuration 2, 4–8, 11, 16, 24, 35, 39, 43, 45, 46, 50, 51, 76, 77, 79, 83, 168, 174, 176, 182, 192–194, 197–199, 202, 208, 213, 219–222 Continuous models 192, 202 Continuous region 192–198, 201, 202 conversion efficiency 205, 206, 214 coordinated 41, 222 Coulomb exchange 43 coverage 38, 39, 48 CPU 4, 15 critical 11–13, 36, 169, 219 cross-platform 13 crystal 45, 59, 123, 136, 139, 140, 141, 144, 145, 147, 167, 207, 222, 224 cytosine 117–121, 123–127, 140 d0 37, 38 Daftardar-Gejji and Jafari Method 93, 94, 96–98 dangling 34, 41, 56, 221, 222 data communications data structure 2, 6, 11, 202–203 decomposition 5, 15, 94 density functional perturbation theory (DFPT) 48 density functional theory (DFT) 8, 24, 25, 27–30, 32–34, 36, 39, 40, 44–48, 50, 51, 60, 118, 120, 130, 135, 159, 174–176, 180–185, 206– 208, 213–215, 219, 221, 223, 225 descriptors 175, 176, 180, 181, 183, 184 deterministic 2, 17 diamond shaped 32 dielectric 27, 28, 29, 31–36, 50 dipole Moment 56, 135, 180 Dirac cone 25, 45, 57, 58, 219, 220, 225 Dirac point 24, 36, 45, 48, 225 Dirac states 219 discretisation strategies for HSD 196 discretisation strategy 196, 197 disordered 31, 41 dissociation energy 79, 80, 85, 182 distributed 3, 4, 7, 10, 15, 193, 198, 199 DNA 118, 119, 135 domain decomposition dosage form 163–170 double ionisation spectrum of SiN 86 double photoionisation spectra 72, 76 double vacancy 42 doublet 83, 87 driving force 205, 207, 211 Dye sensitized solar cells (DSSC) 205–215 EELS 27, 29, 31, 51 efficiency of solar devices 206 egress analysis 202 electric-field 24, 28, 30, 38, 42, 48, 50, 51 electron configurations 80, 85 electron injection 205–207, 211–213, 215 electronegativity 47, 176, 180, 185 electron-energy-loss-spectra (EELS) 23, 27 electronic 24–27, 30, 31, 39, 42–50, 53–60, 71–90, 173–175, 180–185, 206–209, 212, 214, 215, 221–225 electronic delocalization 208, 209 electronic injection 206, 215 electronic properties 24–27, 46, 53–58, 60, 173–175, 180, 206, 208, 220, 221, 223 electronic states 27, 53, 72, 76, 77, 79, 80, 81, 82, 83, 86, 88, 89, 224, 225 electronic states of SiN 83, 86 electrophilic 153, 176, 180, 182, 183 electrophilicity index 176, 180, 182, 183 embedded 2, 9, 17, 42, 43 energy of conduction band 181, 207 energy-dispersive grazing incidence x-ray diffraction 222, 225 enol 119, 135, 136 environment 9, 10, 15–17, 57, 118, 122, 165, 167, 168, 193, 202 epitope – Database 170 equation 2, 10, 34, 77, 80, 93–114, 118, 125, 130, 133, 135, 140, 167, 207, 208 equilibrated 35 equilibration step 17 Index equilibrium distances 77, 80, 83, 85 evacuation dynamics 202 evacuation simulation tools 191 evacuation times 199–201 evacuee profile attributes 199 excitation 27, 29, 31, 36, 48, 50, 76, 208 excited singlet state 208 excited state oxidation potential 207, 212 execution 5, 7, 9, 10 exothermic 48 experimental mass spectrum 82, 88, 90 explicit 10 extraction energy 17 Eyring plot 157 farm 9, 15 F.C factors 79 Fermi velocity 36, 53 fermions 53, 59 ferromagnetic 38–41, 54 fetch fill factor 206 Fine node models 200 fine structure 36, 50 finnis-sinclair 2, 13, 17 5-BrCy 120, 136, 137, 144–147 5-bromocytosine 117, 120, 123, 136, 144–147 5-chlorocytosine 120, 123, 139–141 5-ClCy 120, 139, 142, 144 flow rate of agents 200 flow to density equations 195 force field 2, 8, 17 4-(4’-nitro)phenoxy-7-nitrobenzofurazan 154, 157 4-chloro-7-nitrobenzofurazan 153 4-nitrophenol 154 four peaks assignment 82, 90 Fourier transform-infrared (FT-IR) 168 free energy change 211–213 freestanding 219, 224, 225 frequency/frequencies 3, 27–31, 36, 45, 51, 56, 118, 136, 147, 159, 221 FTIR 36, 117, 168 Ga 49–52 gaming 3, gas phase 71, 118, 123, 125, 130, 133, 135, 137, 147, 206–212 Gaussian 76, 78, 122, 159, 175, 223 231 geometrical setup 198 geometry 24, 33, 44, 125, 135, 173, 175, 176, 180, 192–198, 201 germanene 25, 26, 36, 44, 45–51, 59, 60, 223–225 Germanium (Ge) 23, 25, 44–46, 48–50, 223, 224 GGA 27, 46, 50, 52, 205, 206, 208, 213, 215 glass transition temperature 166 Godson-T gold 45, 174, 175, 180 GPU 4, 14–15, 18 granularity of spatial types 192 graphdiyne 57–60 graphene 23–27, 30, 32, 36, 38, 39, 41–45, 48, 50–53, 55–60, 223 graphical user interface 16 graphyne 57–60 Green’s function 27 grid 16 ground state configurations 174, 177–179 Haeckelites 58 hardness 174, 176, 180, 181, 183 harmonic 50, 77, 80, 82, 85, 118, 122, 123, 136 harmonic wavenumber 80, 85, 118 heap 4, 11 heat dissipation hexagonal 24, 25, 32, 35, 44, 48, 49, 53, 57, 58, 219 high-buckled 24, 220 high performance computing hollow site (HS) 43, 44 HOMO 53, 174, 175, 180–184, 205, 208, 209, 214–215 homogeneity 2, 5, 9, 15, 192, 196, 197 HOMO-LUMO 174, 175, 180–185, 208, 210 honeycomb 44, 45, 47, 48, 221–224, 226 HPC 6, 16, 17 hybrid/hybridization 5, 6, 9, 15, 24–27, 30, 38, 44, 57, 58, 191–203, 225 Hybrid model 199–202 Hybrid Spatial Discretisation (HSD) 192–196 hydrogen 25, 47, 53, 54, 136, 140, 147, 165, 166, 168, 207, 213 hydrogenated 25, 30, 38, 46, 47 i3 i5 i7 imaginary 27, 28, 29, 31, 33, 35, 36, 50, 159 232 Index imino 119, 120, 130 incident photon to current efficiency 206 incomplete gamma functions 95, 109, 111 Infrared (IR) 35, 50, 55, 56, 117, 118, 123, 125, 126, 127, 130, 131, 133, 135, 136, 137, 139, 141, 142, 145, 147, 166, 168, 219 in-plane 56, 122, 223 instance 3–6, 10–12, 15, 16, 153, 173, 196, 202 instrumental methods 166–169 interacting sub core models 193 inter-band 31 intercalation 220 interconnect 3, 6, 7, 9, 14 interface 4, 8, 11, 13–15, 36, 198, 202, 220 internet 3, 9, 15, 16, 18 intramolecular charge transfer 208, 210, 214 intrinsic carrier mobility (ICM) 45 ionization energy 83, 174, 176 irradiation 37, 58 isomers 57, 118 iteration 8, 11, 17, 94 iterative steps 18 Kekule-distortion 57 keto 119, 124, 125, 136 kinetic parameters 159 kinetics 153, 155, 157, 159 LAMMPS 9, 15 large rail tunnel case 192 LDA 27, 46, 47, 175 Light harvesting efficiency (LHE) 207, 208, 210, 211, 215 linear dispersion 54 LINPACK 10 load 8, 15 localization 27, 42, 208, 209, 215 longitudinal 36 look-ahead low lying electronic states 72 low lying isomers 72, 76, 83 low-buckled 24, 46, 219, 220 lowest dissociation limits 83 LSE procedure 133, 135, 141 LUMO 53, 174, 175, 180–184, 205, 208, 209, 214–215 lyophilization 165, 166, 168 M06-2X 157–159 magnetic moment 37–44, 48, 174 magnetic/magnetism 23–60, 117, 154, 166, 173, 174, 175 maintenance many-body 27, 34, 50, 175 material science 8, 30, 174, 175 mathematical model MATLAB 10 matrix 10, 28, 118, 125, 126, 130, 133, 136, 148 memory 4–11, 14–16, 93, 122 meta-stable 52 MgBr2 219, 220 MgS2+ and SiN2+ dications 89 MgS2+ 72, 79, 82, 88, 89 modelling of geometry 196 modular architecture 193 modularity 8, 11, 15 molecular dynamics (MD) 1–18, 35, 167, 175, 221 moment 11, 37–44, 48, 56, 72, 78, 80, 88 monoclonal antibodies 163, 164, 168, 169 Monte-Carlo 2, 17, 18 Moore’s law 3, Moroccan research program 90 MoS2 219, 220 MPI 9, 11, 13, 14, 18 MRCI dissociation 79 MRCI PECs 78 multiconfigurational character of wave functions 76 multilayer 59, 222–223, 225 multiple compartment geometry 193, 194 multiple sequence alignments 166 multiplicity 76, 80 multithreading 10 mutation 118, 135, 163, 169, 170 nanoalloy 173–185 nano-electronics 45, 55, 60, 174 nanomesh 35 nanoribbons 55, 57–59 nanosensors 53 nanotechnology 8, 24, 173, 174 Navigational Graph 194–196, 202 neutral 72, 76, 79, 80, 82, 83, 85–87, 173–185 neutral clusters 185 neutral MgS 72, 76 NMR 117, 154, 155, 166, 167, 168 NMR integrals 155, 157, 159 Index node 3, 4, 13, 14, 16, 17, 45, 191–193, 195–198, 200, 202 normal mode 123, 125, 130 nucleic acid 117–147 nucleophilic aromatic substitution 153 odd-even 55, 182, 183 omp 13 open circuit voltage 206 OpenMP 9, 11, 13 optical 24, 25, 26, 27, 29–33, 35, 36, 38, 43, 50, 51, 58, 59, 60, 173–175, 181, 185 optical-conductivity 29, 31, 50, 51 optimization 6, 15, 51, 175, 176, 208 opto-electronics 33, 45, 46, 51, 57, 60 organosilicon 35 oscillator strength(s) 50, 205, 207, 211, 215 ovalbumin 168 overall factor 134 overlap 25, 82, 168 packing 166, 197 pantograph equation 93–114 paradigm 7–8, 10–17 parallel/parallelism/parallelization 5–11, 13, 15, 17, 30, 31, 35, 45 Pariser-Parr-Pople (PPP) 32 particle collection 11 PECs discussion 71, 89 PED 127, 136 penta-graphene 57, 59 peptide – immunogenic 170 peripheral 16 perturbation 2, 27, 48, 50, 76, 167, 180 photovoltaics 45 physisorption 213 pipelining plasma 31, 51 platform 6, 10, 11, 13, 16, 24, 164, 193, 196, 197, 202 plug-and-play polarization 28, 30, 31, 33, 35, 51, 125, 176, 206, 213 polyatomic 118 porting 15 POSIX 11 potential curves of ground state 76 potential energy curves 71, 72, 76, 79, 85, 88, 89, 136, 157–159, 167 233 pragma 11, 13 preclinical 164 prefetch preformulation 163–170 preservative 163, 166, 169 principal component analysis 168 processing power 3, 14, 15 processor 2–7, 9, 10 program package 122 proprietary interconnect 14 protein – stability 165, 166 – secondary structure 165 – unfolding 169 – conformation 168 – degradation immunogenicity 170 – ultrafast folding 167 – potential energy 167 – crystal 167 – hot spot 163, 169 – globular 163, 169–170 protein aggregation 165–170 protein folding – thermodynamic parameters 163, 166 public 3, 18 quantum 3, 24, 37, 54, 56, 71, 118, 120, 126, 173, 175, 224 quantum spin Hall effect 24, 224 quartet states 84 radius of convergence 104 Raman modes 23, 34, 45, 55, 56, 58, 59, 117, 123, 125, 139, 141, 142, 145–148, 168 rate of formation 155 reconfiguration reflectivity 29, 51 refractive index 28, 29, 31, 50, 51 regression 182 remote compute 15 render farm 15 repository 7, 18 resonant 50, 205 ring mode 123, 127, 131 RISC 14 RNA 118, 135 rotational constants 78, 80, 85 rovibrational levels 80 234 Index scalability 5, 9, 14 scaling 1, 5, 9, 118, 122–123, 125, 126, 130, 133, 135, 136, 140 scanning tunnelling microscopy 25 scheduling 5, 16 script 15 SDRAM second-order kinetics 157 second-order rate 155 semiconductor 3, 24, 25, 27, 45, 48, 54, 55, 57, 189, 206, 207, 208, 211 semimetals 58, 224 sensor 48 serial 7, 8, 12 shaped 32, 43, 46 Short circuit photocurrent densities 206 σ-complex 155, 157, 158 silicene 24, 25, 26, 30–46, 50, 53, 59, 60, 219, 221, 222, 223, 224, 225 SIMD set simulated vibrational spectrum 78, 83 simulation 1–17, 27, 41, 55, 76, 80, 86, 136, 140, 141, 144, 145, 167, 169, 191–199, 221 simulation of vibrationnal spectra of SiN 89 simulation of the vibrational double ionisation 148 simulation set up 199 simulation time SiN 72, 76, 83, 86, 87, 88, 89, 90 singlevacancy 33 skeletal mode 123 SNAr mechanism 153–155, 159 softness 176, 180–184 solvent effects 125, 212 spatial 5, 48, 169, 191–198, 202 spatial aggregation propensity 169 spatial representation 191, 192, 196, 197, 202 spatiotemporal scale spawning 11, 12 spectra 23, 27, 30–32, 36, 37, 50, 51, 55, 56, 71, 72, 76, 89, 90, 117–120, 123, 125, 135, 136, 141, 145, 147, 154, 155, 167, 168, 175, 205– 209, 212, 215 spectral correlation coefficient 168 spectroscopic constants 71, 72, 77, 80, 88, 89 spectroscopy – NMR 166, 168 – FTIR 36, 168 – confocal Raman 168 spectrum 31, 33, 35, 36, 72, 78–80, 82, 83, 86– 90, 117, 118, 123, 130, 135–136, 139, 141, 145, 147, 148, 207 speedup 4, 6, 9, 17, 18 spin-orbit (SO) 24, 85, 220, 224 spin-orbit coupling (SOC) 85, 220 spin-orbit gap 224 spintronic 24, 37, 38, 44, 57 splitting 48 squarographenes 58 stability 23, 25, 38, 42, 47, 52, 53, 55, 57, 93, 94, 101–102, 163–166, 168, 170, 175, 180, 182, 219, 221 standalone 3, 4, 6–10, 17 stationary points 159 statistical coupling analysis 166 strained 38, 43, 223 streamlining 13 streams 10, 200 stretching 56, 130, 133, 135, 147 structure/structural 2, 6, 8, 11, 17, 24–27, 30– 33, 35, 36, 38–40, 44–60, 71, 117, 118–120, 125, 139, 140, 144, 147, 153, 158, 159, 165– 171, 174–176, 180, 182, 185, 194, 196, 202, 203, 206–211, 213, 215, 219, 221, 223, 224 sublattice 24, 25, 30, 47, 49, 53, 221 substitution 153–159, 166, 210, 220 substrate 24, 25, 45, 46, 59, 60, 153, 154, 207, 211, 219, 221, 224, 225, 226 sunlight-to-electricity conversion efficiency 206, 214 supercomputing 3, superhalogens 43 supervisory 16 surface differential reflectance spectroscopy (SDRS) 36 sutton-chen 2, 13, 17 symmetry 24, 26, 30, 32, 47, 48, 51, 52, 56, 57, 60, 76, 79, 80, 81, 87, 88, 90, 123, 176, 180, 181, 183, 184, 221 symmetry group 76, 176, 180–181, 183, 184 tautomer/tautomerism 117–120, 123, 125, 126, 130, 135–137, 139, 144, 145 tensile strain 40 tetragonal 26, 51, 52, 57, 213, 223 tetramer 136, 139, 140, 141, 144–147, 174, 180 T-graphene 59 Index theoretical spectrum 86 therapeutic protein 163–170 – high throughput screening 169 thermo-chemistry cycle 85 thermodynamic parameters 163, 166 thermodynamically 26, 52 third-party 4, 17 thread 5–7, 9–12 tight binding 51, 223 TiO2 nanoparticle 207 topological insulators 24, 224, 225 top-site (TS) 44 transition metal (TM) 25, 39, 43 transition momenta spectrum 72, 78 transition regions 198, 203 transitions 31, 35, 78, 80, 82, 86, 87, 88, 90, 168, 176, 210 transition states 157, 159 transitions between doublets 87 transmittance 36 transport 24, 55 transverse 24, 36 two-dimensional (2D) 23, 221, 224, 225, 226 Ubuntu 17 unit cell 40, 44, 48, 49, 53, 55, 57, 58, 123, 136, 139, 140, 141, 144, 224 235 unstrained 40 uracil 117–120, 123, 126, 130, 131, 134 valence band maximum (VBM) 35 van der Waals 30, 59, 219, 220 verification of HSD 198 verlet-stormer velocity 18 vibration 35, 56, 72, 77, 78, 80, 82, 86, 89, 117–148, 159, 165, 168, 175 vibrational levels 72, 78, 80, 82 vibrational mode 56, 122, 124 vibrational spectroscopy 117–147 vibrational spectrum 78, 80, 117, 118, 130, 135 virtualization 15–18 Watson and Crick 118 wavelength of maximum absorption 209 wavenumbers 55, 56, 77, 80, 85, 118, 120, 122, 123, 125, 126, 130, 133, 135, 136, 139, 140, 141, 145, 147 WSe2 219, 221 X-ray 36, 120, 136, 144, 166, 222 X-ray diffraction 120, 166, 222 zigzag trigonal 32 ...Ponnadurai Ramasami (Ed.) Computational Sciences Also of interest Computational Strong-Field Quantum Dynamics Intense Light-Matter Interactions Bauer (Ed.), 2017 ISBN 978-3-11-041725-8, e-ISBN... are currently planning for the VCCS -2017 to be held from 1st to 31st August 2017 https://doi.org/10.1515/9783110467215-202 VI Preface Prof Ponnadurai Ramasami Computational Chemistry Group, Department... Multiscaling Schmauder, Schäfer (Eds.), 2016 ISBN 978-3-11-041236-9, e-ISBN (PDF) 978-3-11-041245-1 Computational Sciences Edited by Ponnadurai Ramasami Editor Prof Ponnadurai Ramasami University of Mauritius