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Introduction Introduction Why to use a simulation Some examples - PowerPoint PPT Presentation

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Introduction Introduction Why to use a simulation Some examples of questions we can address Molecular Simulations MD Molecular dynamics : solve equations of motion r 1 Monte Carlo : r 2 importance sampling r n Calculate

  1. Introduction

  2. Introduction Why to use a simulation • Some examples of questions we • can address

  3. Molecular Simulations MD • Molecular dynamics : solve equations of motion r 1 • Monte Carlo : r 2 importance sampling r n • Calculate thermodynamic MC and transport properties for a given intermolecular r 1 potential r 2 r n 3

  4. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  5. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system We assume the interactions between the particles are known! 4

  6. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  7. Uses of Molecular Simulations Exact= in the limit of infinitely long simulations the error bars can be made infinitely small The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  8. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  9. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system Pressure Heat capacity Heat of adsorption Structure …. 4

  10. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  11. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system Diffusion coefficient Viscosity … 4

  12. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  13. Uses of Molecular Simulations If one could envision an experimental system of these N particles that interact with the potential. The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  14. Uses of Molecular Simulations The idea for a given intermolecular potential “ exactly” compute the thermodynamic and transport properties of the system 4

  15. Why Molecular Simulations 5

  16. Why Molecular Simulations Paul Dirac, after completing his formalism of quantum mechanics: “ The rest is chemistry…”. 5

  17. Why Molecular Simulations Paul Dirac, after completing his formalism of quantum mechanics: “ The rest is chemistry…”. 5

  18. Why Molecular Simulations Paul Dirac, after completing his formalism of quantum mechanics: “ The rest is chemistry…”. This is a heavy burden the shoulders of “chemistry”: 5

  19. Why Molecular Simulations Paul Dirac, after completing his formalism of quantum mechanics: “ The rest is chemistry…”. This is a heavy burden the shoulders of “chemistry”: 5

  20. Why Molecular Simulations Paul Dirac, after completing his formalism of quantum mechanics: “ The rest is chemistry…”. This is a heavy burden the shoulders of “chemistry”: 5

  21. Intermolecular potential 6

  22. Intermolecular potential The intermolecular potential can: 6

  23. Intermolecular potential The intermolecular potential can: Mimic the experimental system as • accurate as possible: Replace experiments (dangerous, • impossible to measure, expensive, …) 6

  24. Intermolecular potential The intermolecular potential can: Mimic the experimental system as • accurate as possible: Replace experiments (dangerous, • impossible to measure, expensive, …) Make a model system: • Test theories that can not directly be • tested with experiment 6

  25. If we know/guess the “true” intermolecular potential

  26. Example 1: Mimic the “real world” Critical properties of long chain hydrocarbons 8

  27. Example 1: Mimic the “real world” Critical properties of long chain hydrocarbons To predict the thermodynamic properties (boiling points) of the hydrocarbon mixtures it is convenient (=Engineering models use them) to know the critical points of the hydrocarbons. 8

  28. Critical points of long chain hydrocarbons Pentane 9

  29. Critical points of long chain hydrocarbons Heptadecane Pentane 9

  30. Hydrocarbons: intermolecular potential United-atom model Fixed bond length • CH 2 CH 2 CH 3 Bond-bending • CH 2 CH 3 Torsion • Non-bonded: Lennard- • Jones 10

  31. OPLS (Jorgensen) Model

  32. Vapour-liquid 
 equilibria 12

  33. Vapour-liquid 
 equilibria Computational issues: 12

  34. Vapour-liquid 
 equilibria Computational issues: How to compute • vapour-liquid equilibrium? 12

  35. Vapour-liquid 
 equilibria Computational issues: How to compute • vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? 12

  36. Vapour-liquid 
 Molecular dynamics: press enter and see … equilibria Computational issues: How to compute • vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? 12

  37. Vapour-liquid 
 Molecular dynamics: press enter and see … equilibria Computational issues: How to compute • vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? Molecular dynamics: press enter and see … 12

  38. Vapour-liquid 
 But my system is Molecular dynamics: press extremely small, is enter and see … equilibria the statistic reliable? Computational issues: How to compute • vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? Molecular dynamics: press enter and see … 12

  39. Vapour-liquid 
 But my system is Molecular dynamics: press extremely small, is enter and see … equilibria the statistic reliable? Computational issues: How to compute • vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? Molecular dynamics: But C48 moves much slower press enter and see … than methane (C1). Do I have enough CPU time 12

  40. Vapour-liquid 
 But my system is Molecular dynamics: press extremely small, is enter and see … equilibria the statistic reliable? Lectures on Free Computational issues: Energies and How to compute • Phase Equilibrium vapour-liquid equilibrium? How to deal • with long chain hydrocarbons? Molecular dynamics: But C48 moves much slower press enter and see … than methane (C1). Do I have enough CPU time 12

  41. Vapour-liquid 
 But my system is Molecular dynamics: press extremely small, is enter and see … equilibria the statistic reliable? Lectures on Free Computational issues: Energies and How to compute • Phase Equilibrium vapour-liquid Lectures on equilibrium? advanced Monte How to deal • Carlo with long chain hydrocarbons? Molecular dynamics: But C48 moves much slower press enter and see … than methane (C1). Do I have enough CPU time 12

  42. Critical Temperature and Density Nature 365 , 330 (1993). 13

  43. Example 2 Methane Storage

  44. Methane cars: the technological obstacle Gasoline, 1 liter CH 4 1 liter 0.036 MJ 34.2 MJ

  45. Methane versus gasoline LNG CNG Makal et a l. Chem. Soc. Rev. 2012 41.23, 7761-7779.

  46. 65 bar 5.8 bar Insufficient flow ~1 bar P H = 65 bar P L = 5.8 bar

  47. The deliverable capacity P L P H = 5.8 bar = 65 bar Methane adsorbed Methane adsorbed (v STP/v) (v STP/v) at tank charging at tank discharge pressure pressure ARPA-E (DOE) target: 315 m 3 STP methane/m 3 adsorbent

  48. An optimal heat of adsorption? Goal: maximize deliverable capacity

  49. An optimal heat of adsorption? Goal: maximize deliverable capacity ( ) opt = H 0 exp − q iso RT H CH 4

  50. An optimal heat of adsorption? Goal: maximize deliverable capacity ( ) opt = H 0 exp − q iso RT H CH 4

  51. An optimal heat of adsorption? Goal: maximize deliverable capacity “For methane, an optimal enthalpy change of [16.2] kJ/mol is found.” ( ) opt = H 0 exp − q iso RT H CH 4

  52. In silico screening of zeolites MFI expt’l data: Sun et al . (1998) J. Phys. Chem. B. 102(8), 1466-1473. Zhu et al. (2000) Phys. Chem. Chem. Phys. 2(9), 1989-1995. Force field: Dubbeldam et al . (2004) Phys. Rev. 93(8), 088302.

  53. In silico screening of zeolites C. Simon et al . (2014) Phys. Chem. Chem. Phys . 16 (12), 5499-5513

  54. Enthalpy vs. entropy Δ S not the same for all materials • Wide range of Δ H that yields optimal material •

  55. Can we find a material that meets the DOE target? Screening > 100,000 materials zeolites • Metal organic Frameworks, MOFs (Snurr and • co-workers) zeolitic imidazolate frameworks, ZIFs, • (Haranczyk) Polymer Porous Networks, PPNs (Haranczyk) •

  57. Insight from the model Empty tank

  58. Example 3: make a model system 26

  59. Example 3: make a model system Question: are attractive interactions needed to form a solid phase? 26

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