Enhancing Computational Capabilities in Chemistry (and Grid Computing) at UNCW

1. Enhancing ComputationalCapabilities in Chemistry (and Grid Computing) at UNCW Ned H. Martin Department of Chemistry and Biochemistry University of North Carolina Wilmington Duke University, April 18, 2005

2. Outline, Part 1 • Culture of technology use in Chemistry at UNCW • Grants that provided necessary infrastructure • Phase I of Integrating Modeling into Curriculum: Goals and Strategy • Selective Integration of Modeling into most promising course / instructor combinations to enhance student’s 3D perceptions. • Demonstration of benefits (to win support of faculty). • Phase II of Integrating Modeling into Curriculum • Expand to other courses in chemistry. • Current Efforts / Results / Conclusions

3. Early Use of Technology at UNCW • 1981 – First student microcomputer lab at UNCW (Chemistry) • Spreadsheets, statistics, graphing, word processing. • ProStat statistical analysis/graphing software written by Dick Ward • 1986 – Chemical Applications of Microcomputers course • Introduced students to word processing, spreadsheets, and interfacing computers with electronic equipment • 1988 – Molecular modeling software obtained • PCModel on pcs, • AMPAC on VAX (gift from Dewar’s group); initially used only in research.

4. Early Use of Technology at UNCW • 1989 – NHM attended NSF Workshop on Molecular Modeling • Week-long workshop at Georgia State University. • 1990 – Computational Chemistry courses at NCSC & online • Provided necessary competence/confidence level for faculty to initiate teaching of computational chemistry methods. • 1992 – Introduced Computational Chemistry into Advanced • Organic Chemistry (Physical Organic) course • Used computations to illustrate concepts in text; students did not do calculations themselves, just saw results

5. Grants for Infrastructure • 1992 – HyperChem grants in Chemistry and Biochemistry • Software for curriculum development, research. • 1993 – NSF Grant for Integrating Molecular Modeling • into the Chemistry Curriculum (“Phase I”) • Provided SGI workstation, 8 ‘fast’ pcs, and multiple copies of HyperChem modeling software for chemistry student computer lab and faculty. • Impacted primarily upper level chemistry courses: Organic Chemistry, Advanced (Physical) Organic, Physical Chemistry, Biochemistry, Independent Study.

6. Grants for Infrastructure… • 1994 – NIDA Medication Development Database • Pilot project contract; provided Accord (3D structural database software), student training, led to QSAR projects • 1996 – ACS-PRF grant for Modeling NMR Shielding (#1) • Spartan and Gaussian94W software, student support • 1997 – NCSC Visualization grant (to NHM) • SGI O2 workstation, AVS visualization software • 1998 – NCSC Visualization grant (to MM) • SGI O2 workstation, AVS visualization software

7. Grants for Infrastructure… • 2000 – ACS-PRF grant for Modeling NMR Shielding (#2) • Updated modeling software, student support • 2000 – Camille and Henry Dreyfus Grant to Enhance • Computational Chemistry Capabilities (“Phase II”) • Impacted courses omitted from 1992 NSF grant: Introductory (General) Chemistry, Inorganic Chemistry, Medicinal Chemistry, and a new course in Computational Chemistry. • Also addressed student research needs, NMR data processing. • Provided SGI workstation, NMR analysis software, 10 fast pcs, multiple copies of Titan.

8. Grants for Infrastructure… • 2001 – Numina Grant for HP Jornadas` and pocket HyperChem • Allowed student use of computers fro molecular modeling in class; also allowed for instant feedback on student perceptions • 2002 – ITSD grant for PocketPCs • Improved in-class devices • 2004 – ACS-PRF grant for • Modeling NMR Shielding (#3) • Updated software, student support Modeling NMR Shielding (#3)

9. Goals and Strategy, Phase I • Goal (Phase I): To enhance student’s perception of 3D concepts in chemistry: • Stereochemistry; conformations of molecules, and relationship of energy to molecular conformation. • Strategy 1: Selective integration of modeling into the most promising course/instructor combinations(most receptive) • Acceptance by the instructor is key. This sometimes required some time for the “value” of computational chemistry to be recognized. • Training is also needed for those not using modeling in research. This must be repeated each semester for new instructors and TAs.

10. Goals and Strategy, Phase I… • Strategy 2: Progressively integrate molecular modeling into the chemistry curriculum, starting in sophomore Organic Chemistry • Include some modeling in several courses throughout the curriculum, so that students learn a variety of applications • Verify modeling predictions with experimental results • Teach increasing levels of theory as needed, rather than overloading students with theory to start • Treat molecular modeling as a routine tool, like GC, HPLC, IR, or NMR • Design experiments so that students can “discover” applications of molecular modeling as well as learn its limitations

11. Specific Objectives, Phase I • Develop computational exercises with experimentally verifiable results for selected courses. • Predicting the major alkene isomer resulting from dehydration of an alcohol. (Organic Chemistry) • Base pair H-bonding stabilizes DNA. (Biochemistry) • Test student’s perception/knowledge level before and after modeling was introduced to determine the effect of the curriculum change. • Provide adequate and ongoing instructional / tutorial support for students and faculty/TAs. • Gain support and confidence of faculty.

12. Intro. to Molecular Mechanics • Organic Chemistry students learn the basics of molecular mechanics • Create models of structures, perform energy minimizations • Measure bond lengths, bond angles, and dihedral angles • Construct model of axial methylcyclohexane using “ideal” bond lengths and bond angles; measure these. • Perform energy minimizations and observe how the molecule adjusts its structure to minimize its energy; measure the same bond lengths and bond angles after energy minimization. 109.5° 112.2°

13. Organic Chemistry Experiment • Compute the energies of the isomeric carbocations that arise from acid-catalyzed dehydration of an alcohol. (2º carbocation) methide shift (3º carbocation) Sayed, Y.; Ahlmark, C. A.;Martin, N. H. J. Chem. Educ. 1989, 66, 174-175.

14. Organic Chemistry Experiment… • Computation shows that the rearranged 3º carbocation is much lower in energy; it can lose H+ to form either of two alkenes; the one that predominates according to GLC analysis is the lower energy alkene, also shown by calculation. major product; lower energy minor product; higher energy Martin, N. H. J. Chem. Educ.1998, 75, 241-243.

15. Biochemistry Experiment • Students model pairs of DNA bases (C-G, A-T, as well as others) using semi-empirical MO theory; they determine the strength of the H-bonds; C-G (top, which forms three H-bonds), has the greatest stabilization due to H-bonding; A-T (bottom) forms only 2 H-bonds.

16. Biochemistry Experiment… • A plot of the mol % C-G vs. the literature value of ‘melting temperatures’ (temperature at which the helix unravels) of various DNA samples is linear. • This demonstrates the effect of H-bonding on stabilizing the double helix. Martin, N. H., Burgess, S. K., Connelly, T. L., Reynolds, W. R.; Spiro, L. D. Biochemical Education1996, 24(4), 230-231.

17. Specific Objectives, Phase II • Develop computational exercises with experimentally verifiable results for additional selected courses. • Shapes of simple molecules; VSEPR rule ‘verification’. (General Chemistry) • Orbital shapes and energies; transition metal complexes. (Inorganic Chemistry) • Relating electrostatic energy to stability in carbocations. (Physical Organic Chemistry) • Develop new Computational Chemistry course. • Provide ongoing instructional / tutorial support for students and faculty/TAs.

18. Bond angle calculation General Chemistry • Hand-held Dell Axim PocketPCs (left) runing HyperChem provide students with in-class opportunity to view and rotate 3D structures, measure bond angles, and examine molecular shapes and resulting properties, such as polarity.

19. Experimental group used HyperChem to rotate molecules and measure bond angles

20. Control groupused the PocketPCs to view structures in color, but with no rotation capability

21. Sample Quiz Questions

22. Test Results VSEPR Questions Gas Law Question (control)

23. Inorganic Chemistry • HP Jornadas or PocketPCs and HyperChem are used in Inorganic (CHM 445) lecture to visualize molecular orbital splitting, see the shapes of molecular orbitals and their energy levels, and calculate bond stretching frequencies of CO before and after complexation with a metal.

24. Inorganic Chemistry… • Students compute the energies of the molecular orbitals of BH3 (top) and then visualize them (bottom) to assess Lewis acid properties.

25. Physical Organic Chemistry • Students use Jornadas or PocketPCs and HyperChem during lecture to examine various topics as they are discussed, including: • MO calculations of molecular geometry, bond orders, atomic charges, and hybridization. • Visualization of symmetry properties of molecules: • Calculation and visualization of steric effects in substituted cyclohexanes. • Students also do computational projects outside of class using HyperChem on pcsin the computer lab.

26. Computational Chemistry course

27. Computational Chemistry… • New course in 2002, 2 lecture & 2 computer lab hours/wk • http://www.uncwil.edu/chem/molecularmodeling • Covers the basic theoretical background of several computational methods: molecular mechanics, quantum mechanics, density functional theory, molecular dynamics. • Provides computer lab exercises in model building, energy minimization,conformation searching, transition state modeling, reaction pathway modeling, visualization of results and molecular property calculations (NMR). • Introduces solvent effects, QSAR, modeling biomolecules, UNIX language, grid computing.

28. Comp. Chem Syllabus • Introduction to computational chemistry (overview of capabilities, relative cpu time, limitations and applications of various methods) • Molecular mechanics (components of force fields, file types, atom types, successes and limitations; caveats about minimum energy structure) • LAB 1. Building and optimizing structures in Titan (model building, rendering modes, measurements) • Molecular orbital theory, part 1 (history, levels of MO theory, SEMO methods, computational results) • LAB 2. Manual conformation searching methods • Molecular orbital theory, part 2 (ab initio MO theory, basis sets, correlated methods, effect of choice of method/basis set on cpu time) • LAB 3. Automated conformation searching • Calculating molecular properties (energy derivatives, UV-Vis, NMR, freq.)

29. Comp. Chem Syllabus • LAB 4. Ring strain in cycloalkanes; isodesmic reactions • Potential energy surfaces; optimization methods; reaction path following (gradient, stationary points, saddle point, minimization algorithms, TS modeling, frequency calculation, rxn. pathway calc.) • LAB 5. Modeling a reaction pathway; the pinacol rearrangement (locating a TS; frequency calculation; • Computing charges on atoms (Mulliken, natural bond order, AIM, MK and CHELPG charges; best fit to NMR data; electrostatic effects on carbocation stabilization and conformation) • LAB 6. Stability of alkenes and carbocations • Solvation effects; hybrid (QM/MM) methods (explicit, continuum and hybrid models; ONIUM method; hybrid MM/QM methods) • LAB 7. Basicity of amines (electrostatic potential mapped on electron density isosurface; modeling solvent effects) • LAB 8. Modeling bromonium ion intermediates (LUMO)

30. Comp. Chem Syllabus • Density functional theory (guest lecturer Lee Bartolotti, ECU) • LAB 9. Endo/Exo Selectivity in Diels-Alder Cycloadditions (kinetic vs thermodynamic control) • Grid Computing; UNIX operating system; Remote computing; Gaussian 03; GridNexus; NMR calculations of classical vs. non-classical carbocations • LAB 10. Modeling the Relative Acidities of Substituted Phenols (npa charges, electrostatic potential mapped on electron density isosurface) • Quantitative Structure-Activity Relationships (QSAR) • LAB 11. NMR shift and charge calculations using Gaussian 03 on a Linux cluster; Introduction to Grid computing viaGridNexus (file formats and their interconversion) • WWW computational chemistry resources; modeling biomolecules (special visualization methods)

31. Summary and Conclusions, Part 1 • Computational applications have been integrated throughout the chemistry curriculum at UNCW. • The process requires interested/convinced faculty. • Ongoing training of faculty and TAs is critical. • We found that to be most effective, computer exercises should be verified by laboratory results. • Integration into multiple courses and all levels (freshman through senior level) is critical in order to demonstrate to students the general applicability of computational methods.

32. Part 2. Grid Computing

33. Rationale for Grid Computing The recent proliferation* of fast, interconnectedunderutilized cpus ts/104 * over 150,000,000 pcs are sold each year!

34. A computing Grid is analogous to an electrical power grid. The user simply “taps” into the resource (with permission), but is usually unaware of the origin of the resource. Grid Computing

35. Grid Computing at UNCW • Current efforts by a group of UNCW computer science faculty and undergraduate students, plus faculty and students in several “application areas” are focused on developing a graphical user interface (GUI) called • GridNexus serves as a front-end to simplify data manipulations, searching or calculations of various types performed on remote computers over a Grid. • This project has grant support from the UNC Office of the President

36. GridNexus • GridNexus is based on JXPL, a new graphical programming language developed by UNCW computer science faculty and students. • GridNexus allows users to link modules that perform various operations into a usable ‘workflow’, then save these for later use. • Once a ‘workflow’ has been created, one only need to specify the path/filename of the data set to be operated on and the path/filename for the output file. • This greatly simplifies repetitive operations, and takes much of the mystery out of computing for non-computer science users.

37. File Interconversion in GridNexus • One of the limitations of most computational chemistry software packages is that they do not read or write many different (proprietary) file types, so it is difficult to transfer data from one program to another. • GridNexus allows users to input some of the most common types of geometry specification, such as .pdb (.ent) and .mol files, and use a default set of options (or select from a list) to write a Gaussian input (.dat) file. • GridNexus alsoallows the user to orient a molecule in a specified way in Cartesian coordinates.

38. Gaussian 03 under GridNexus Functions can be selected from lists at the top left, dragged onto the workspace and joined. The entire workflow can be hidden in a single multifunction box

39. Gaussian 03 under GridNexus Submitting a Gaussian job can be as simple as selecting the input file name (from a variety of file types) and the desired output file name.

40. Molecule Orientation in GridNexus • One module allows a molecule to be oriented in Cartesian space in a specified way, then writes a proper Gaussian03 input file.

41. Gaussian 03 Input File • %chk=tmp/martinn/phenanthreneNH2.chk • # HF/6-31G(d,p) opt freq • phenanthreneNH2 • 0 1 • H -1.963715 -3.198017 1.280991 • C -1.127512 -2.730904 0.750482 • H -0.184242 -4.593909 0.244859 • C -0.149560 -3.501921 0.166986 • C 0.000000 -0.715690 0.000000 • N 0.000000 0.715690 0.000000 • C 0.908090 -2.892498 -0.536779 • C -1.036579 -1.338948 0.691052 • C 0.971979 -1.491079 -0.702775 • C 1.943981 -3.742718 -1.057698 • H -1.800364 -0.744862 1.210005 • H 1.238823 1.070292 -1.769705 • C 2.993024 -3.223318 -1.730309 • (etc.) Note C & N along the Y axis, the midpoint of their bond at the origin

42. What’s next for GridNexus? • Develop more “filters” to transform data. • Enhance the graphics for appearance and usability. • Include more software applications. • Extend Grid services to other disciplines. • Include industry and businesses as users and developers. • Add more computational nodes to the Grid. The goal is to include all NC institutions of higher learning

43. Acknowledgements • NSF • ACS-PRF • HyperCube, Inc. • Pearson Education Foundation • Camille and Henry Dreyfus Foundation • UNCW: Department of Chemistry and Biochemistry, College of Arts and Sciences, Division of Academic Affairs, and Information Technology Systems Division (ITSD) • (former) North Carolina Supercomputing Center • UNC-Office of the President