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PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids

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Abstract

Visualizing vibrational motions calculated with different ab initio packages requires dedicated post-processing tools. Here, we present a PyMOL plugin called PyVibMS for visualizing the vibrational motions for both molecular and solid systems calculated by mainstream quantum chemical computer programs including Gaussian, Q–Chem, VASP, and CRYSTAL. Benefiting from the continuing development of the PyMOL platform, PyVibMS provides powerful functionalities and user-friendly interface. PyVibMS was written in Python and its open-source nature makes it flexible and sustainable. As an example, the motions of the Konkoli-Cremer local vibrational modes are shown in this work for the first time. PyVibMS is freely available at https://github.com/smutao/PyVibMS.

In this work, a PyMOL plugin named PyVibMS is developed to visualize molecular and lattice vibrations.

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References

  1. Chalmers J (2002) Handbook of vibrational spectroscopy. Wiley, New York

    Google Scholar 

  2. Larkin P (2018) Infrared and Raman spectroscopy: principles and spectral interpretation. Elsevier, Amsterdam

    Google Scholar 

  3. Meier RJ (2005) Vibrational spectroscopy: a ‘vanishing’ discipline?. Chem Soc Rev 34(9):743

    CAS  PubMed  Google Scholar 

  4. Krimm S, Bandekar J (1986) Vibrational spectroscopy and conformation of peptides, polypeptides, and proteins. In: Advances in protein chemistry, vol 38. Elsevier, pp 181–364

  5. Pouchert C (1997) The Aldrich library of FT-IR spectra. Aldrich, Milwaukee

    Google Scholar 

  6. Paschoal VH, Faria L FO, Ribeiro M CC (2017) Vibrational spectroscopy of ionic liquids. Chem Rev 117(10):7053–7112

    CAS  PubMed  Google Scholar 

  7. Bakker HJ, Skinner JL (2010) Vibrational spectroscopy as a probe of structure and dynamics in liquid water. Chem Rev 110(3):1498–1517

    CAS  PubMed  Google Scholar 

  8. Skinner JL, Pieniazek PA, Gruenbaum SM (2011) Vibrational spectroscopy of water at interfaces. Acc Chem Res 45(1):93–100

    PubMed  PubMed Central  Google Scholar 

  9. Perakis F, Marco LD, Shalit A, Tang F, Kann ZR, Kühne TD, Torre R, Bonn M, Nagata Y (2016) Vibrational spectroscopy and dynamics of water. Chem Rev 116(13):7590–7607

    CAS  PubMed  Google Scholar 

  10. Wilson EB, Decius JC, Cross PC (2012) Molecular vibrations: the theory of infrared and Raman vibrational spectra. Dover Publications, Mineola

    Google Scholar 

  11. Bratos S, Pick RM (1980) Vibrational spectroscopy of molecular liquids and solids. Springer, Boston

    Google Scholar 

  12. Mitra SS (1962) Vibration spectra of solids. In: Solid state physics. Elsevier, pp 1–80

  13. Tarte P (1990) Vibrational spectroscopy and solid state chemistry. Solid State Ion 42(3-4):177–196

    CAS  Google Scholar 

  14. Sherwood PMA (2011) Vibrational spectroscopy of solids. Cambridge University Press, Cambridge

    Google Scholar 

  15. Zou W (2020) UniMoVib: A unified interface for molecular harmonic vibrational frequency calculations. https://github.com/zorkzou/UniMoVib

  16. Ochterski JW (1999) White paper: vibrational analysis in gaussian. Gaussian

  17. Martinez M, Gaigeot M-P, Borgis D, Vuilleumier R (2006) Extracting effective normal modes from equilibrium dynamics at finite temperature. J Chem Phys 125(14):144106

    CAS  PubMed  Google Scholar 

  18. Brehm M, Kirchner B (2011) TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories. J Chem Inf Model 51(8):2007–2023

    CAS  PubMed  Google Scholar 

  19. Thomas M, Brehm M, Fligg R, Vöhringer P, Kirchner B (2013) Computing vibrational spectra from ab initio molecular dynamics. Phys Chem Chem Phys 15(18):6608–6622

    CAS  PubMed  Google Scholar 

  20. Gastegger M, Behler J, Marquetand P (2017) Machine learning molecular dynamics for the simulation of infrared spectra. Chem Sci 8(10):6924–6935

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Hafner J (2008) Ab-initio simulations of materials using vasp: density-functional theory and beyond. J Comput Chem 29(13):2044–2078

    CAS  PubMed  Google Scholar 

  22. Dovesi R, Pascale F, Civalleri B, Doll K, Harrison NM, Bush I, D’Arco P, Noël Y, Rérat M, Carbonnière P, Causà M, Salustro S, Lacivita V, Kirtman B, Ferrari AM, Gentile FS, Baima J, Ferrero M, Demichelis R, Pierre M DL (2020) The CRYSTAL code, 1976-2020 and beyond, a long story. J Chem Phys 152(20):204111

    CAS  PubMed  Google Scholar 

  23. Eck B (2019) wxDragon Version 2.1.7. http://www.wxdragon.de/

  24. Kokalj A (1999) XCrySDen-a new program for displaying crystalline structures and electron densities. J Mol Graph Model 17(3-4):176–179

    CAS  PubMed  Google Scholar 

  25. Kokalj A (2003) Computer graphics and graphical user interfaces as tools in simulations of matter at the atomic scale. Computat Mater Sci 28(2):155–168

    CAS  Google Scholar 

  26. Miranda H (2019) Phonon website - visualize phonon vibrational modes, GitHub. http://henriquemiranda.github.io/phononwebsite/

  27. Noël Y (2018) Animation of vibrational modes and Simulated IR/Raman spectra with CRYSTAL. http://crysplot.crystalsolutions.eu/web_pages_yves3/vibration.html

  28. Beata G, Perego G, Civalleri B (2019) CRYSPLOT: a new tool to visualize physical and chemical properties of molecules, polymers, surfaces, and crystalline solids. J Comput Chem 40(26):2329–2338

    CAS  PubMed  Google Scholar 

  29. DeLano WL (2002) The PyMOL molecular graphics system. DeLano Scientific, San Carlos, CA, USA

  30. Schrödinger LLC (2017) The PyMOL molecular graphics system. Version 2.0

  31. Atilgan AR, Durell SR, Jernigan RL, Demirel MC, Keskin O, Bahar I (2001) Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys J 80(1):505–515

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Grell L, Parkin C, Slatest L, Craig PA (2006) EZ-Viz, a tool for simplifying molecular viewing in PyMOL. Biochem Mol Biol Educ 34(6):402–407

    CAS  PubMed  Google Scholar 

  33. Medek P, Beneš P, Sochor J (2007) Computation of tunnels in protein molecules using Delaunay triangulation. J WSCG 15:107–114

    Google Scholar 

  34. Hodis E, Schreiber G, Rother K, Sussman JL (2007) eMovie: a storyboard-based tool for making molecular movies. Trends in Biochem Sci 32(5):199–204

    CAS  Google Scholar 

  35. Ordog R (2008) PyDeT, A PyMOL plug-in for visualizing geometric concepts around proteins. Bioinformation 2(8):346–347

    PubMed  PubMed Central  Google Scholar 

  36. Steinkellner G, Rader R, Thallinger GG, Kratky C, Gruber K (2009) VASCo: computation and visualization of annotated protein surface contacts. BMC Bioinform 10(1):32

    Google Scholar 

  37. Lua RC, Lichtarge O (2010) PyETV: a PyMOL evolutionary trace viewer to analyze functional site predictions in protein complexes. Bioinformatics 26(23):2981–2982

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Seeliger D, de Groot BL (2010) Ligand docking and binding site analysis with PyMOL and AutoDock/Vina. J Comput Aided Mol Des 24(5):417–422

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Cabrera AC, Gil-Redondo R, Perona A, Gago F, Morreale A (2011) VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface. J Comput Aided Mol Des 25(9):813–824

    CAS  PubMed  Google Scholar 

  40. Lua RC (2012) PyKnot: a PyMOL tool for the discovery and analysis of knots in proteins. Bioinformatics 28(15):2069–2071

    CAS  PubMed  Google Scholar 

  41. Martin OA, Vila JA, Scheraga HA (2012) CheShift-2: graphic validation of protein structures. Bioinformatics 28(11):1538–1539

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Faure G, Andreani J, Guerois R (2012) InterEvol database: exploring the structure and evolution of protein complex interfaces. Nucleic Acids Res 40(D1):D847–D856

    CAS  PubMed  Google Scholar 

  43. Hagelueken G, Ward R, Naismith JH, Schiemann O (2012) MtsslWizard: in silico spin-labeling and generation of distance distributions in pymol. Appl Magn Reson 42(3):377–391

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Bramucci E, Paiardini A, Bossa F, Pascarella S (2012) PyMod: sequence similarity searches, multiple sequence-structure alignments, and homology modeling within PyMOL. BMC Bioinform 13(Suppl 4):S2

    Google Scholar 

  45. Pasi M, Tiberti M, Arrigoni A, Papaleo E (2012) xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures. J Chem Inf Model 52(7):1865–1874

    CAS  PubMed  Google Scholar 

  46. Sehnal D, Vařeková RS, Berka K, Pravda L, Navrátilová V, Banáš P, Ionescu C-M, Otyepka M, Koča J (2013) MOLE 2.0: advanced approach for analysis of biomacromolecular channels. J Cheminform 5(1):39

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Bachega J FR, Timmers L F SM, Assirati L, Bachega LR, Field MJ, Wymore T (2013) GTKDynamo: a PyMOL plug-in for QC/MM hybrid potential simulations. J Comput Chem 34(25):2190–2196

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Makarewicz T, Kaźmierkiewicz R (2013) Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J Chem Inf Model 53(5):1229–1234

    CAS  PubMed  Google Scholar 

  49. Oberhauser N, Nurisso A, Carrupt P-A (2014) MLP tools: a PyMOL plugin for using the molecular lipophilicity potential in computer-aided drug design. J Comput Aided Mol Des 28(5):587–596

    CAS  PubMed  Google Scholar 

  50. Hu B, Lill MA (2014) WATsite: hydration site prediction program with PyMOL interface. J Comput Chem 35(16):1255–1260

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Patel H, Gruning BA, Gunther S, Merfort I (2014) PyWATER: a PyMOL plug-in to find conserved water molecules in proteins by clustering. Bioinformatics 30(20):2978–2980

    CAS  PubMed  Google Scholar 

  52. Oliveira SauloHP, Ferraz FelipeAN, Honorato RV, Xavier-Neto J, Sobreira Tiago JP, de Oliveira Paulo SL (2014) KVFinder: steered identification of protein cavities as a pymol plugin. BMC Bioinform 15(1):197

    Google Scholar 

  53. Warnecke A, Sandalova T, Achour A, Harris RA (2014) PyTMs: a useful PyMOL plugin for modeling common post-translational modifications. BMC Bioinform 15(1):370

    Google Scholar 

  54. Gaudreault F, Morency L-P, Najmanovich RJ (2015) NRGsuite: a PyMOL plugin to perform docking simulations in real time using flexaid. Bioinformatics 31(23):3856–3858

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Chaudhari R, Li Z (2015) PyMine: a PyMOL plugin to integrate and visualize data for drug discovery. BMC Res Notes 8(1):517

    PubMed  PubMed Central  Google Scholar 

  56. Baumgartner M (2016) Improving rational drug design by incorporating novel biophysical insight. Ph.D. Thesis, University of Pittsburgh

  57. Panjkovich A, Svergun DI (2016) SASpy: a PyMOL plugin for manipulation and refinement of hybrid models against small angle x-ray scattering data. Bioinformatics 32(13):2062–2064

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Makarewicz T, Kaźmierkiewicz R (2016) Improvements in GROMACS plugin for pymol including implicit solvent simulations and displaying results of PCA analysis. J Mol Model 22(5):109

    PubMed  PubMed Central  Google Scholar 

  59. Arroyuelo A, Vila JA, Martin OA (2016) Azahar: a PyMOL plugin for construction, visualization and analysis of glycan molecules. J Comput Aided Mol Des 30(8):619–624

    CAS  PubMed  Google Scholar 

  60. Sridhar A, Ross GA, Biggin PC (2017) Waterdock 2.0: water placement prediction for holo-structures with a PyMOL plugin. PLOS ONE 12(2):e0172743

    PubMed  PubMed Central  Google Scholar 

  61. Masand VH, Rastija V (2017) PyDescriptor : a new PyMOL plugin for calculating thousands of easily understandable molecular descriptors. Chemom Intell Lab Syst 169:12–18

    CAS  Google Scholar 

  62. Janson G, Zhang C, Prado MG, Paiardini A (2017) PyMod 2.0: improvements in protein sequence-structure analysis and homology modeling within PyMOL. Bioinformatics 33(3):444–446

    CAS  PubMed  Google Scholar 

  63. Gierut AM, Niemyska W, Dabrowski-Tumanski P, Sułkowski P, Sulkowska JI (2017) PyLasso: a PyMOL plugin to identify lassos. Bioinformatics 33(23):3819–3821

    CAS  PubMed  Google Scholar 

  64. Yuan S, Chan HS, Hu Z (2017) Using PyMOL as a platform for computational drug design. Wiley Interdiscip Rev Comput Mol Sci 7(2):e1298

    Google Scholar 

  65. Jurrus E, Engel D, Star K, Monson K, Brandi J, Felberg LE, Brookes DH, Wilson L, Chen J, Liles K, Chun M, Li P, Gohara DW, Dolinsky T, Konecny R, Koes DR, Nielsen JE, Head-Gordon T, Geng W, Krasny R, Wei G-W, Holst MJ, McCammon JA, Baker NA (2018) Improvements to the APBS biomolecular solvation software suite. Protein Sci 27(1):112–128

    CAS  PubMed  Google Scholar 

  66. Jarmolinska AI, Zhou Q, Sulkowska JI, Morcos F (2019) DCA-MOL: a PyMOL plugin to analyze direct evolutionary couplings. J Chem Inf Model 59(2):625–629

    CAS  PubMed  Google Scholar 

  67. Gierut AM, Dabrowski-Tumanski P, Niemyska W, Millett KC, Sulkowska JI, Valencia A (2019) PyLink: a PyMOL plugin to identify links. Bioinformatics 35(17):3166–3168

    CAS  PubMed  Google Scholar 

  68. Lu X-J (2020) DSSR-enabled innovative schematics of 3d nucleic acid structures with PyMOL. Nucleic Acids Res 48(13):e74. https://doi.org/10.1093/nar/gkaa426

    Article  PubMed  PubMed Central  Google Scholar 

  69. Tilley RJD (2006) Crystals and crystal structures. Wiley, New York

    Google Scholar 

  70. Born M, Huang K (1954) Dynamical theory of crystal lattices. Clarendon Press, Oxford

    Google Scholar 

  71. Fang T, Li Y, Li S (2017) Generalized energy-based fragmentation approach for modeling condensed phase systems. Wiley Interdiscip Rev Comput Mol Sci 7(2):e1297

    Google Scholar 

  72. Civalleri B, Pascale F, Noel Y (2017) Vibrational frequencies calculation. CRYSTAL 2017

  73. Ferrabone M, Baima J (2017) Phonon dispersion with CRYSTAL. CRYSTAL 2017

  74. Kittel C (2004) Introduction to solid state physics. In: Crystal Vibrations. Wiley, p 99

  75. Pascale F, Zicovich-Wilson CM, Gejo FL, Civalleri B, Orlando R, Dovesi R (2004) The calculation of the vibrational frequencies of crystalline compounds and its implementation in the CRYSTAL code. J Comput Chem 25(6):888–897

    CAS  PubMed  Google Scholar 

  76. Holder T (2013) Cgo arrow. Accessed: 2019-06-19. https://pymolwiki.org/index.php/Cgo_arrow

  77. Zou W, Kalescky R, Kraka E, Cremer D (2012) Relating normal vibrational modes to local vibrational modes with the help of an adiabatic connection scheme. J Chem Phys 137(8):084114

    PubMed  Google Scholar 

  78. Zou W, Cremer D (2014) Properties of local vibrational modes: the infrared intensity. Theor Chem Acc 133:1451–1466

    Google Scholar 

  79. Tao Y, Zou W, Cremer D, Kraka E (2017) Correlating the vibrational spectra of structurally related molecules: a spectroscopic measure of similarity. J Comput Chem 39(6):293–306

    PubMed  Google Scholar 

  80. Tao Y, Tian C, Verma N, Zou W, Wang C, Cremer D, Kraka E (2018) Recovering intrinsic fragmental vibrations using the generalized subsystem vibrational analysis. J Chem Theory Comput 14(5):2558–2569

    CAS  PubMed  Google Scholar 

  81. Tao Y (2018) Advances in local vibrational mode theory and Unified Reaction Valley Approach (URVA). Ph.D. Thesis, Southern Methodist University. Chemistry theses and dissertations. 2. https://scholar.smu.edu/hum_sci_chemistry_etds/2

  82. Tao Y, Zou W, Sethio D, Verma N, Qiu Y, Tian C, Cremer D, Kraka E (2019) In situ measure of intrinsic bond strength in crystalline structures: local vibrational mode theory for periodic systems. J Chem Theory Comput 15(3):1761– 1776

    CAS  PubMed  Google Scholar 

  83. Cremer D, Wu A, Larsson JA, Kraka E (2000) Some thoughts about bond energies, bond lengths, and force constants. J Mol Model 6(4):396–412

    CAS  Google Scholar 

  84. Kalescky R, Kraka E, Cremer D (2013) Identification of the strongest bonds in chemistry. J Phys Chem A 117(36):8981–8995

    CAS  PubMed  Google Scholar 

  85. Zou W, Cremer D (2016) C2 in a box: determining its intrinsic bond strength for the X1 Σ+g Ground State. Chem Eur J 22(12):4087–4097

    CAS  PubMed  Google Scholar 

  86. Zhang X, Dai H, Yan H, Zou W, Cremer D (2016) B-H⋅⋅⋅ π interaction: a new type of nonclassical hydrogen bonding. J Am Chem Soc 138(13):4334–4337

    CAS  PubMed  Google Scholar 

  87. Tao Y, Zou W, Jia J, Li W, Cremer D (2017) Different ways of hydrogen bonding in water - why does warm water freeze faster than cold water?. J Chem Theory Comput 13(1):55–76

    CAS  PubMed  Google Scholar 

  88. Cremer D, Kraka E (2017) Generalization of the tolman electronic parameter: the metal-ligand electronic parameter and the intrinsic strength of the metal-ligand bond. Dalton Trans 46(26):8323–8338

    CAS  PubMed  Google Scholar 

  89. Tao Y, Zou W, Kraka E (2017) Strengthening of hydrogen bonding with the push-pull effect. Chem Phys Lett 685:251–258

    CAS  Google Scholar 

  90. Tao Y, Zou W, Cremer D, Kraka E (2017) Characterizing chemical similarity with vibrational spectroscopy: new insights into the substituent effects in monosubstituted benzenes. J Phys Chem A 121(42):8086–8096

    CAS  PubMed  Google Scholar 

  91. Verma N, Tao Y, Marcial BL, Kraka E (2019) Correlation between molecular acidity (pka) and vibrational spectroscopy. J Mol Model 25(2):48

    PubMed  Google Scholar 

  92. Tao Y, Qiu Y, Zou W, Nanayakkara S, Yannacone S, Kraka E (2020) In situ assessment of intrinsic strength of X-I⋯OA type halogen bonds in molecular crystals with periodic local vibrational mode theory. Molecules 25 (7):1589

    CAS  PubMed Central  Google Scholar 

  93. Verma N, Tao Y, Zou W, Chen X, Chen X, Freindorf M, Kraka E (2020) A critical evaluation of Vibrational Stark Effect (VSE) probes with the local vibrational mode theory. Sensors 20(8):2358

    CAS  Google Scholar 

  94. Konkoli Z, Cremer D (1998) A new way of analyzing vibrational spectra. I. Derivation of adiabatic internal modes. Int J Quantum Chem 67(1):1–9

    CAS  Google Scholar 

  95. Konkoli Z, Larsson JA, Cremer D (1998) A new way of analyzing vibrational spectra. II. Comparison of internal mode frequencies. Int J Quantum Chem 67(1):11–27

    CAS  Google Scholar 

  96. Konkoli Z, Cremer D (1998) A new way of analyzing vibrational spectra. III. Characterization of normal vibrational modes in terms of internal vibrational modes. Int J Quantum Chem 67(1):29–40

    CAS  Google Scholar 

  97. Konkoli Z, Larsson JA, Cremer D (1998) A New way of analyzing vibrational spectra. IV. Application and testing of adiabatic modes within the concept of the characterization of normal modes. Int J Quantum Chem 67(1):41–55

    CAS  Google Scholar 

  98. Kraka E, Cremer D (2019) Dieter cremer’s contribution to the field of theoretical chemistry. Int J Quantum Chem 119(6):e25849

    Google Scholar 

  99. Kraka E (2019) Preface: dieter cremer’s scientific journey. Mol Phys 117(9-12):1047–1058

    CAS  Google Scholar 

  100. Kraka E, Zou W, Tao Y (2020) Decoding chemical information from vibrational spectroscopy data – local vibrational mode theory. WIREs: Comput Mol Sci 10(5):e1480. https://doi.org/10.1002/wcms.1480

    Google Scholar 

  101. Stephens PJ, Devlin FJ, Chabalowski CF, Frisch MJ (1994) Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J Phys Chem 98(45):11623–11627

    CAS  Google Scholar 

  102. Dunning TH (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 90(2):1007–1023

    CAS  Google Scholar 

  103. Woon DE, Dunning TH (1993) Gaussian basis sets for use in correlated molecular calculations. III. The atoms aluminum through argon. J Chem Phys 98(2):1358–1371

    CAS  Google Scholar 

  104. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EYN, Kudin KN, Staroverov VN, Keith TYA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16 Revision B.01. Gaussian Inc. Wallingford CT

  105. Dasgupta S, Herbert JM (2017) Standard grids for high-precision integration of modern density functionals: sg-2 and sg-3. J Comput Chem 38(12):869–882

    CAS  PubMed  Google Scholar 

  106. Kraka E, Zou W, Filatov M, Tao Y, Grafenstein J, Izotov D, Gauss J, He Y, Wu A, Konkoli Z, Polo V, Olsson L, He Z, Cremer D (2020) COLOGNE2020. see http://www.smu.edu/catco

  107. Kresse G, Hafner J (1993) Ab initio molecular dynamics for liquid metals. Phys Rev B 47 (1):558–561

    CAS  Google Scholar 

  108. Kresse G, Hafner J (1994) Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys Rev B 49(20):14251–14269

    CAS  Google Scholar 

  109. Kresse G, Furthmüller J (1996) Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mater Sci 6(1):15–50

    CAS  Google Scholar 

  110. Kresse G, Furthmüller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54(16):11169–11186

    CAS  Google Scholar 

  111. Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77(18):3865

    CAS  PubMed  Google Scholar 

  112. Perdew JP, Burke K, Ernzerhof M (1997) Erratum: generalized gradient approximation made simple [Phys. Rev. Lett. 77, 3865 (1996)]. Phys Rev Lett 78(7):1396–1396

    CAS  Google Scholar 

  113. Blöchl PE (1994) Projector augmented-wave method. Phys Rev B 50(24):17953

    Google Scholar 

  114. Kresse G, Joubert D (1999) From ultrasoft pseudopotentials to the projector augmented-wave method. Phys Rev B 59(3):1758

    CAS  Google Scholar 

  115. Monkhorst HJ, Pack JD (1976) Special points for brillouin-zone integrations. Phys Rev B 13 (12):5188–5192

    Google Scholar 

  116. Baroni S, deGironcoli S, Corso AD, Giannozzi P (2001) Phonons and related crystal properties from density-functional perturbation theory. Rev Mod Phys 73(2):515–562

    CAS  Google Scholar 

  117. Dovesi R, Erba A, Orlando R, Zicovich-Wilson CM, Civalleri B, Maschio L, Rérat M, Casassa S, Baima J, Salustro S, Kirtman B (2018) Quantum-mechanical condensed matter simulations with crystal. Wiley Interdiscip Rev Comput Mol Sci 8(4):e1360

    Google Scholar 

  118. Dovesi R, Saunders VR, Roetti C, Orlando R, Zicovich-Wilson CM, Pascale F, Civalleri B, Doll K, Harrison NM, Bush IJ, D’Arco P, Llunell M, Causà M, Noël Y, Maschio L, Erba A, Rerat M, Casassa S (2017) CRYSTAL17 User’s Manual. University of Torino, Torino

  119. Zhao Y, Truhlar DG (2008) The m06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four m06-class functionals and 12 other functionals. Theor Chem Acc 120(1):215–241

    CAS  Google Scholar 

  120. Dill JYD, Pople JA (1975) Self-consistent molecular orbital methods. XV. Extended gaussian-type basis sets for lithium, beryllium, and boron. J Chem Phys 62(7):2921–2923

    CAS  Google Scholar 

  121. Hehre WJ, Ditchfield R, Pople JA (1972) Self-consistent molecular orbital methods. xii. Further extensions of gaussian-type basis sets for use in molecular orbital studies of organic molecules. J Chem Phys 56 (5):2257–2261

    CAS  Google Scholar 

  122. Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chem Acc 28(3):213–222

    CAS  Google Scholar 

  123. Togo A, Tanaka I (2015) First principles phonon calculations in materials science. Scr Mater 108:1–5

    CAS  Google Scholar 

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Acknowledgments

We thank SMU for providing supercomputing resources. Y.T. thanks Yue Qiu and **n Chen for valuable comments.

Funding

This work was financially supported by National Science Foundation Grants CHE 1464906.

Author information

Authors and Affiliations

  1. Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX, 75275-0314, USA

    Yunwen Tao, Sadisha Nanayakkara & Elfi Kraka

  2. Shaanxi Key Laboratory for Theoretical Physics Frontiers, Institute of Modern Physics, Northwest University, **’an, Shaanxi, 710127, People’s Republic of China

    Wenli Zou

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  1. Yunwen Tao
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  2. Wenli Zou
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  3. Sadisha Nanayakkara
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  4. Elfi Kraka
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Correspondence to Elfi Kraka.

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Appendices

Appendix 1: Installation guide

The following instructions describe the installation of the latest version of PyVibMS:

  1. 1.

    Install the latest version of PyMOL 2.x from pre-compiled binaries or source code;

  2. 2.

    Download the latest version of PyVibMS from GitHub repository;

  3. 3.

    Open PyMOL and navigate to the Plugin Manager menu under the Plugin button;

  4. 4.

    On the Install New Plugin tab, select the __init__.py file in the PyVibMS folder after clicking the Choose file button. This step loads PyVibMS into PyMOL;

  5. 5.

    The PyVibMS item will be added to the Plugin menu if properly installed. Clicking PyVibMS opens its GUI window.

Appendix: 2: Format of the user-provided mode file

If the user wants to visualize the local vibrational modes calculated by LMODEA program or molecular/lattice vibrations calculated by a different package outside the supported ones listed in this work, an additional text file can be read like the following.

The 1st line contains two integer numbers: the first is the number of atoms N in a molecule or in a primitive/unit cell for solid systems, while the second number specifies the number of additional vibrations this text file has.

The 2nd and 16th lines are the blank lines before the information of each vibration.

The 3rd and 17th lines are the header of each vibration, and each line has four fields. The first field takes either L or N, representing local vibration and normal vibration respectively. The second field is the vibrational frequency in wavenumbers. The third field gives the irreducible representation of current vibration. Noteworthy is that local vibrations have no symmetry; therefore, we provide 0 here. The last field takes a string which will show up in the comment column in the table section of the GUI window.

Lines 4–15 and 18–29 list 3N atomic displacements of vibrations in Cartesian coordinates for the N atoms.

The 30th line of END following the displacement information of the last vibration denotes the end of this text file.

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Tao, Y., Zou, W., Nanayakkara, S. et al. PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids. J Mol Model 26, 290 (2020). https://doi.org/10.1007/s00894-020-04508-z

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  • DOI: https://doi.org/10.1007/s00894-020-04508-z

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