1. 1. Welcome! Heather J. Kulik hjkulik@mit.edu Thu. 09-04-14 10.637 quantum chemical simulation
2. 2. MIT 10.637 Lecture 1 Outline Introduction to simulations Survey Course overview: content and assignments Introductions from students: what are you hoping to learn? Case studies in simulations XSEDE and lab technical details
3. 3. MIT 10.637 Lecture 1 Atomistic simulations give us a view of how materials, catalysts, and chemical systems behave on the nanoscale (both in time and space). Quantum chemistry uses approximations to the Schrodinger equation to describe the behavior of electrons around nuclei to give us a first principles view of chemical bonding and bond-breaking. Nanoreactor simulations of gas phase collisions Properties of salt solutions confined in nanotubes What are simulations?
4. 4. MIT 10.637 Lecture 1 Why simulations? Protein folding: how proteins fold and misfold (Prof. Vijay Pande) Voelz, Bowman, Beauchamp, Pande. JACS (2010).
5. 5. MIT 10.637 Lecture 1 Why simulations? Drug design: 2nd generation HIV protease inhibitor Kaletra See Cobb Biomedical Computational Review 2007 and references therein.
6. 6. MIT 10.637 Lecture 1 Why simulations? Photochemistry for RNA bases: mechanisms for alternative proton transfer between RNA bases. Golan et al. Nature Chem. (2012).
7. 7. MIT 10.637 Lecture 1 Materials science Materials Genome Project: Identifying elements that substitute for each other, chemical trends Hautier, G Ceder, G. Chemistry of Materials (2010).
8. 8. MIT 10.637 Lecture 1 Choosing a computational model Empirical models functional form with parameters from experimental or other calculated data: Pair potentials Many body potentials Semi-empirical models model Hamiltonians: Tight binding MNDO, AM1 Quantum mechanical models approximations to the Schrdinger equation: Hartree-Fock Density functional theory Post-Hartree-Fock (Configuration interaction, MP2) Moreefficient Moretransferable Best tool for the job? Depends on the job!
9. 9. MIT 10.637 Lecture 1 When we need quantum Potential energy surfaces: explicit or for force field development Bonding and structure: from first principles experiment QM
10. 10. MIT 10.637 Lecture 1 When we need quantum Interesting phenomena depend on what the electrons are doing! Optical properties Catalysis Magnetic properties
11. 11. MIT 10.637 Lecture 1 A look at the course Lectures: Tu/Th 4-5:30 PM 26-168 Labs: Tu/Th 4-5:30 PM 14-0637 -see class schedule Instructor: Professor Heather J. Kulik Office: E18-558 E-mail: hjkulik@MIT.edu Office Hours: Thu 3 pm or by appt. Website: https://stellar.mit.edu/S/course/10/fa14/10.637/index.html
12. 12. MIT 10.637 Lecture 1 A look at the course Grading: Homework 80% Journal article presentation 20% Undergraduates: Homework only. Homework: Lab assignments are due one week after the in course portion. Policies: Late homework will be accepted up until the time that solutions are posted but late submissions will be eligible for at most half credit.
13. 13. MIT 10.637 Lecture 1 Lectures (18): Cover a diverse number of topics in classical and first- principles simulations. In class labs (6): Classical and first- principles simulations on high performance computers. Introduction to linux and scripting. Homework (80%): Six labs with short answers and demonstration of work. Independent assignment (20%): journal article and simulation at the end of the course. No exams. Meets Tues, Thu 4-5:30pm, Room 26-168 (14-0637 for labs) Class format
14. 14. MIT 10.637 Lecture 1 Optional background texts F. Jensen Introduction to Computational Chemistry Wiley W. Koch and M. C. Holthausen A Chemists Guide to Density Functional Theory Wiley-VCH A. Szabo and N. S. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory Dover C. J. Cramer Essentials of Computational Chemistry: Theories and Models Wiley M. P. Allen and Tildesley Computer simulations of Liquids Oxford Science Publishers T. Schlick Molecular Modeling and Simulation Springer D. A. McQuarrie and J. D. Simon Physical Chemistry: A molecular approach University Science Books D. Frenkel and B. Smit Understanding Molecular Simulations: From Algorithms to Applications Academic Press Specific literature will be provided if necessary on Stellar.
15. 15. MIT 10.637 Lecture 1 Socrative m.socrative.com Room: 122778
16. 16. MIT 10.637 Lecture 1 Energy functions (force fields), molecular mechanics, geometry optimizations, potential energy surfaces, and classical molecular dynamics Theory and application of first-principles computer simulations methods: Hartree-Fock theory and density functional theory. Sampling methods, ab initio molecular dynamics, QM/MM, transition-path finding approaches. Excited state methods: time-dependent density functional theory, correlated wavefunction theory, many-body perturbation theory. Discussion of applications in materials science, biochemistry, and catalysis. stretch bend torsional non-bonded Whats covered?
17. 17. MIT 10.637 Lecture 1 Course layout 1. Classical force fields, geometry optimization and dynamics (Lab 1 and 2). 2. Electronic structure theory: Hartree-Fock theory and Density Functional Theory (Labs 3 and 4). 3. Transition-state theory, Ab initio MD, sampling, and QM/MM (Lab 5 and 6). 4. Advanced wavefunction techniques (TDDFT, perturbation theory, etc) (Lab 5).
18. 18. MIT 10.637 Lecture 1 Course schedule
19. 19. MIT 10.637 Lecture 1 Course schedule
20. 20. MIT 10.637 Lecture 1 Course schedule
21. 21. MIT 10.637 Lecture 1 Upon completion of this course, you will: Be able to assess the relative accuracy and efficiency of simulations methods and know the applicability of these methods (system size, elements, properties, etc). Be able to read and understand computational/simulations literature. Be able to carry out independent research in computational catalysis. Be versed in several codes spanning classical and first-principles, biological and nonbiological simulation. Have exposure to the commandline, linux, and scripting. Outcomes
22. 22. MIT 10.637 Lecture 1 Case studies Nanoreactor: reaction discovery G-Protein coupled receptors Predicting singlet fission Screening surface catalysts
23. 23. MIT 10.637 Lecture 1 Case study: nanoreactor We can predict rate constants for simple reactions using a first-principles, molecular view of how species react. macroscopic microscopic Urey-Miller experiment L.P. Wang, et al., submitted to Nature Chem. (2014).
24. 24. MIT 10.637 Lecture 1 Case study: nanoreactor trr trr trr kmrV i ii 0 0 2 0 2 0 Time (ps) Radius() r r Dt t0 t0+Dt r r How it works, a periodic confining potential enhances reactivity: Techniques used: Ab initio MD with Hartree-Fock theory. Plus transition-state finding approaches: string method, nudged elastic band.
25. 25. MIT 10.637 Lecture 1 Case study: nanoreactor Complex reaction networks:
26. 26. MIT 10.637 Lecture 1 Acetylene nanoreactor
27. 27. MIT 10.637 Lecture 1 Very large final products from starting with many triple bonds Acetylene nanoreactor
28. 28. MIT 10.637 Lecture 1 Case study: G-Protein coupled receptors G-protein-coupled receptors are a family of membrane-bound a-helical proteins. They regulate physiological processes by transmitting signals from extracellular to intracellular. GPCRs are good drug targets. Specific example b2-adrenergic receptor (b2AR) is implicated in diabetes, obesity, and asthma. X-ray structures of the active and inactive states are available. Authors wanted to look at the transition between active and inactive states. Techniques used: Classical molecular dynamics and Markov state models to cluster together thousands of trajectories along with mutual information analysis and docking. K.J. Kohlhoff, et al., Nature Chem. (2014).
29. 29. MIT 10.637 Lecture 1 Case study: G-Protein coupled receptors Active vs. inactive structures: Activating events (highlighted)
30. 30. MIT 10.637 Lecture 1 Case study: G-Protein coupled receptors Used cloud computing to simulate 2.15 ms of dynamics. Run many different simulations starting from both activated and inactive forms of the protein. Clustered the simulations and built 150 ms activation trajectories. When activator (agonist) is present, can stay activated for periods of 1.5-5 ms When inactivator (antagonist) or apo protein is simulated, then no activation events.
31. 31. MIT 10.637 Lecture 1 Case study: G-protein coupled receptors Mutual information is a method to identify correlated residues: the agonist correlates intracellular and extracellular residues, but the inverse agonist disrupts these correlations. Different molecules dock at different activation points in MSM model.
32. 32. MIT 10.637 Lecture 1 Case study: singlet fission Singlet fission (first observed in the 1960s) could allow for solar cells to exceed 100% efficiency and photovoltaics to have power conversion efficiencies of 40%. Occurs in molecular crystals of materials such as pentacene. Occurs via the mechanism: Coupling influences the rate: Energy diagram: Molecule A Molecule B Molecule A Molecule B Molecule A Molecule B singlet fission excited singlet triplet singlet GS
33. 33. MIT 10.637 Lecture 1 Case study: singlet fission S.R. Yost et al., Nature Chem. (2014). Fission occurs very quickly (~80 fs), may be accelerated by charge-transfer super-exchange. Weak-coupling regime Strong coupling regime
34. 34. MIT 10.637 Lecture 1 Case study: singlet fission
35. 35. MIT 10.637 Lecture 1 Case study: singlet fission Techniques used: Density functional theory (PBE0/6-31g*), QM/MM with constraints and a configuration- interaction approach. Good agreement between theory and experiment. Fast fission from Marcus-like expression predicted for slightly exothermic DG and large coupling. Fastest endothermic kfis (tetracene Tc) is 10x slower than slowest exothermic kfis (DPP). Hc is too exothermic Marcus inverted region. Tuning Es-2ET good idea for optimizing singlet fission. S.R. Yost et al., Nature Chem. (2014).
36. 36. MIT 10.637 Lecture 1 Case study: singlet fission Pentacene derivatives: V < 20 meV, they are in the non-adiabatic regime. Above this, they see same fission rate and observe a non-adiabatic to adiabatic transition. S.R. Yost et al., Nature Chem. (2014).
37. 37. MIT 10.637 Lecture 1 Especially relevant for the chemical engineering discipline: Case study: catalysis Techniques used: Plane-wave density functional theory with ultrasoft pseudopotentials 340 eV wavefunction cutoff/500 eV for the charge density, RPBE functional. Greeley et al., Nature Chem. (2009). Computational catalysis screening can identify new catalytic materials. Example: polymer electrolyte membrane fuel cells (PEMFCs) need faster oxygen reduction reaction currently at platinum electrode, but platinum is expensive. ORR catalysts must be stable under corrosive conditions but must be able to activate O2. O2 activation typically occurs via proton and electron transfer to form OOH before O-O bond breaking. Seek out alloys with Pt or Pd overlayers close to Pt3X or Pd3X.
38. 38. MIT 10.637 Lecture 1 Case study: catalysis Greeley et al., Nature Chem. (2009). Minimizing DG will speed up the reaction. Two rate limiting steps are DG1: proton transfer and electron transfer for adsorbed OOH and DG2: removal of OH or O from surface. Both correlate strongly to the stability of O (DEO) on the surface.
39. 39. MIT 10.637 Lecture 1 Case study: catalysis Greeley et al., Nature Chem. (2009). If DEO becomes too positive, then DG1 increases (bad) but DG2 decreases because it becomes easier to break Pt-OH and Pt-O bonds (good). These opposing effects lead to a maximum theoretical turnover relative to DEO on Pt (DEO Pt). If a surface binds O 0.0-0.4 eV more weakly than Pt(111), then it should exhibit ORR activity better than Pt optimum at 0.2 eV. Focused on stable compounds. Correlated to experimental activities to confirm relationship.
40. 40. MIT 10.637 Lecture 1 Case study: catalysis Alloying alters the predicted stability of oxygen binding for some cases: 50% alloying element in 2nd layer (circles) vs 25% alloying element in 2nd layer (squares). The lighter portion of the graph background indicates optimal activity.
41. 41. MIT 10.637 Lecture 1 XSEDE accounts Your xsede account should be the same as your athena username. This will give you access to computers where we will run simulations, so its important you do this ASAP! Point browser to portal.xsede.org
42. 42. MIT 10.637 Lecture 1 Getting started at XSEDE
43. 43. MIT 10.637 Lecture 1 XSEDE machines Maverick: GPU computing at TACC
44. 44. MIT 10.637 Lecture 1 XSEDE machines Trestles: CPU computing at SDSC
45. 45. MIT 10.637 Lecture 1 Feedback svy.mk/1B9fZxs
46. 46. MIT 10.637 Lecture 1 OS X Applications>Utilities>Terminal and add to dock
47. 47. MIT 10.637 Lecture 1 OS X
48. 48. MIT 10.637 Lecture 1 Working with athena Open terminal and put it into your dock. Pick a text editor to use and get comfortable nano for most of you. Try some basic linux commands.
49. 49. MIT 10.637 Lecture 1 Commandline tips ls: lists all visible files in the current directory. Try: ls -ltrh to view files with long printing (l), last modified sorting (t), in reverse (r), and human readable (h) file sizes. Try: ls .* to view all hidden files that start with . such as .bashrc. Try: ls */* to view all files in first layer of subdirectories, etc. Try: ls */ -d to view all files that match a wildcard with a directory.
50. 50. MIT 10.637 Lecture 1 Commandline tips cd: change directory. Try: cd ~/ to change directory to home Try: cd - to change directory to the previous one. Try: cd ../ to move up one directory
51. 51. MIT 10.637 Lecture 1 Commandline tips mkdir: make a directory. Try: mkdir -p path/to/directory to simulatenously make new directory path, with a subdirectory inside to, with sub-subdirectory inside called directory pwd: gets the current working directory. ln: make a symbolic link for a directory. Say you have a long source directory like /usr/local/source/file/compiler/bin/ and want to be able to see its contents more easily. Try: ln -s /usr/local/source/file/compiler/bin/ easydir to make a symbolic link in your existing directory as a subdirectory called easydir.
52. 52. MIT 10.637 Lecture 1 Commandline tips cp/mv: copy or move files from one place to another. You may want to copy or move files around from one place to another. Here are some examples of copying or moving the old file apples.txt to oranges.txt. Try: cp -i apples.txt oranges.txt i for interactive means that if oranges.txt already exists, it will ask you if you want to overwrite. Try: mv -i apples.txt oranges.txt i for interactive means that if oranges.txt already exists, it will ask you if you want to overwrite the existing oranges.txt. Try: mv -f apples.txt oranges.txt f forces the move even if an existing oranges.txt is already there. Try: cp -p apples.txt oranges.txt p means that permissions and timestamps will be preserved. Try: cp -r apples/ oranges/ r means that youre recursively copying all the files in a directory. You need to do this if youre trying to copy a directory.
53. 53. MIT 10.637 Lecture 1 Commandline tips rm: remove a file. Much like cp and mv, you can remove files (be careful with this) using a couple different flags. Try: rm -i apples.txt to interactively i remove a file (i.e. get a y/n statement) Try: rm -v apples.txt to get a verbose listing of the files removed. Try: rm -r apples/ to recursively remove an entire directory.
54. 54. MIT 10.637 Lecture 1 Summary Review on your own, more commandline tips: http://hjklol.mit.edu/content/bios-203-useful- commandline-tools Or check for cheat sheet on Stellar. Make sure you have set up your xsede account before you leave today! Lets get started next time with molecular mechanics! Any questions?
55. 55. MIT 10.637 Lecture 1 Volcano plot from Norskov group (Stanford) predicting turnover frequency of various catalysts.