In the Classroom edited by
Computer Bulletin Board
Steven D. Gammon
Collaborative Physical Chemistry Projects Involving Computational Chemistry
University of Idaho Moscow, ID 83844
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David M. Whisnant* Department of Chemistry, Wofford College, Spartanburg, SC 29303; *
[email protected] Jerry J. Howe Department of Chemistry, Converse College, Spartanburg, SC 29302 Lisa S. Lever Department of Chemistry, University of South Carolina Spartanburg, Spartanburg, SC 29303
If the frequent articles in Chemical and Engineering News (1) are any indication, computational chemistry has come of age as a tool in the laboratory. As computational software moves into the chemistry mainstream, there is a growing need to bring molecular modeling into undergraduate courses. Brown, for instance, has made a convincing case for introducing undergraduates to computational chemistry (2). He concludes that “as computers grow faster and less expensive, and are found increasingly in chemists’ laboratories, computational investigations of chemical systems will be performed by chemists trained in subdisciplines other than computational and theoretical chemistry. In light of these observations, it is imperative that the undergraduate chemistry curriculum introduce students to computational chemistry.” As a part of cooperative efforts to modernize the physical chemistry laboratory experiments at our three schools, we now include molecular modeling projects in both semesters of our courses. These projects introduce students to semiempirical and ab initio molecular orbital calculations and how they can be used in conjunction with experimental observations. We are using 266-MHz Pentium II PCs with 64 or 128 MB of RAM running Windows 95 with Quantum CAChe (3) and Gaussian 94W (4 ) as tools in these projects. Along with the use of modern computational methods, we have given our projects a research flavor by basing them on problems that are most easily solved through collaboration. Online collaboration is a convenient way to facilitate communication among students as the projects develop—especially if more than one college is involved. Following the example of Long and Zielinski (5) almost all of the communication during these two projects was online, either by email or through the World Wide Web. Hair Dyes Project This project involved 26 students during the first semester of the 1997–98 academic year. At the beginning, the project Web site directed the students to play the role of R&D chemists looking for colored compounds that might be used as hair dyes. The search was limited to benzene derivatives with two or three functional groups because their small size helps them penetrate hair fibers easily (6 ). In the first phase of the project, the students were asked to use molecular
modeling to narrow the field of possible compounds down to just a few, which synthetic chemists could investigate later. The first students tackling the problem simply guessed derivatives to try. They optimized the geometry of model molecules with Quantum CAChe PM3 and then predicted their UV–vis spectra using ZINDO CI. Their results were sent by email to one of the instructors, who forwarded the messages to the entire group and posted the results on the project Web site (7 ). Subsequent students used the accumulated experience of the group’s work to make a more informed choice of derivatives to model. In the course of the project more than 29 compounds were investigated. Some of the final compounds, for instance 2,4-dinitroaniline, were similar to compounds actually used as hair dyes (6 ). As the project progressed we encouraged email comments from the students and a discussion of ideas. We also used email to pose questions to the group and to introduce new phases of the project. As one example, after several spectra had been submitted and posted to the Web, a question from one of the students about the calculated spectra led us to ask the group how they could test the reliability of the predictions. Some students suggested that we compare computational predictions with the experimental spectra of actual compounds. This gave us the opportunity to discuss the fact that the calculations were for gas-phase molecules and the effects that solvents have on spectra. Some of the students acquired UV–vis spectra in the laboratory and found that with hexane as the solvent the theoretical spectra reproduced the experimental very well, generally predicting peaks within 10 nm. Because benzene has been listed as a carcinogen (8), we also asked the group about the potential health and environmental effects of these types of compounds. Some students contributed literature references on this subject, which we relayed to the group via email, and others submitted Web sites, which we linked with our project Web site. In the last phase of the project we asked students to propose a pathway for a reasonable synthesis of the most promising compound. Carbon Clusters Project Since the discovery of C60, chemists have extensively investigated the properties of pure carbon molecules (9). Although they are not as stable as the large molecules, smaller
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carbon clusters are of interest because of their possible existence in interstellar space, in the atmospheres of carbon stars, and in vapors above graphite (10). In 1989, scientists from the Kitt Peak National Observatory (11) reported a series of lines around 2164 cm᎑1 in the absorption spectrum of a carbon star, IRC+10216 (listed below in cm᎑1). 2164.347 2164.733 2165.870 2166.241 2166.612 2166.977 2167.339 2167.699 2168.052 2170.613 2171.901 2172.214 2172.525 2173.135 2173.433 2173.731
In our second project, which was completed by 18 students during the second semester of the 1997–98 academic year, we informed the students that the lines probably were due to an infrared transition in a small carbon cluster molecule— probably C3, C4, or C5. They were asked to use computational chemistry to help identify the molecule that was being observed and to infer some of its properties from the spectra and the computational results. As before, communication on this project was almost entirely by email and the project Web site (12). The project is summarized below. WEEK 1 Assembling a list of possible isomers.
WEEK 3 Questions and discussion leading to a preliminary prediction of the most stable isomer for C3, C4, and C5. WEEK 4 Geometry optimization and prediction of vibrational frequencies using ab initio calculations. WEEK 5 Questions and discussion leading to the solution of the problem.
The students usually worked in pairs. The first week of the project was devoted to the identification of possible isomers for the three compounds. In the following week each group of students used the CAChe PM3 model to optimize the geometry and to predict the heat of formation and infrared spectrum for two of the isomers. In the third week we asked the group how the calculated heats of formation could be used to predict the most stable isomer for C3, C4, and C5, if we assume that entropy effects are negligible. The group decided, via an online discussion, that the linear isomers were favored because they had the lowest heats of formation. Finally, by comparing bond lengths predicted from covalent radii tables with those from the semiempirical calculations, the group decided that the Lewis structures involved doubly bonded carbon atoms: C
C
C
C
C
C
C
C
C
C
C
At various times during these projects we emphasized the limitations of computational software (13). At the end of this phase of the project we mentioned the possibility that the PM3 parameterization might not be adequate for pure carbon molecules. Did, in fact, these Lewis structures make any sense? After some discussion, the students concluded that they were sensible. The double bonds give the interior carbon 200
Group 1: Linear C3 at the RHF/6-31G(d) level Group 2: Linear C3 at the MP2/6-31G(d) level Group 3: Linear C4 at the UHF/6-31G(d) level Group 4: Linear C4 at the UMP2/6-31G(d) level
WEEK 2 Prediction of heats of formation of the isomers using semiempirical MO calculations.
C
atoms a full octet, VSEPR theory suggests that each interior carbon would have 180° bond angles, and sp-hybridization indicates a linear geometry. The usual PM3 parameterization is not adequate for carbon clusters (14), although we did not inform the students of this. We did tell them that experimental laser photodetachment and Coulomb imaging studies (15) show that a cyclic isomer exists for C4. Furthermore, ESR spectroscopy has observed a signal for the linear C4 molecule (16 ), indicating that it has unpaired electrons. Because neither of these was predicted by the PM3 model the students decided that we should move to more accurate ab initio calculations, which became the next phase of the project. Using the results of the previous PM3 calculations to help determine input coordinates for Gaussian, groups of students optimized the geometries and predicted the vibrational frequencies of several carbon clusters:
Group 5: Cyclic C4 at the RHF/6-31G(d) level Group 6: Cyclic C4 at the MP2/6-31G(d) level Group 7: C4 (trigonal ring with attached C) at the RHF/6-31G(d)level Group 8: Linear C5 at the RHF/6-31G(d) level Group 9: Linear C5 at the MP2/6-31G(d) level
The results of these calculations—geometries, energies, dipole moments, rotational constants, and both unscaled and scaled (17) frequencies—were distributed to the group. During the last week of the project, prompted by our questions, the group decided that the cyclic C4 isomer is slightly more stable than the linear but that their energies are very close. The C4 structure explored by Group 7 turned out to be a saddle point with one imaginary calculated vibrational frequency. The IR frequencies ruled out C4 but not C3 or C5, for which the major calculated IR frequencies were within 5% of the experimental value. The students also concluded that the lines in the IR spectrum were due to rotational transitions. We then asked the students how the splitting between the rotational lines might help them determine if the observed transition was due to C3 or C5. This created some initial confusion, mainly because of calculation errors and the fact that some rotational lines were missing in the experimental data. Allowing for the missing lines, an average of the splitting between the peaks, which according to the rigid rotor model should be separated by 4B for C3 and C5, gave a value of the rotational constant around 0.086 cm᎑1. The ab initio values for C3 and C5 were 0.43 cm᎑1 and 0.087 cm᎑1, respectively, which indicated that the carbon cluster molecule observed at 2164 cm᎑1 in the carbon star was C5. We asked several questions at the end of the week about IR and Raman active peaks, the possibility of using pure rotational spectroscopy to study the molecules, the point groups of the C5 isomers, the equilibrium between cyclic and linear C4, and the temperature of the region of space in which the C5 molecule is found.
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In the Classroom
Summary
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After a year of working together on these computational projects we are enthusiastic about them. They gave students experience with modern computational models and software and the use of the Internet as a routine tool for communication. Our physical chemistry classes do not cover quantum mechanics until the second semester, so our students used the software with little or no background in computational chemistry. The exposure to quantum theory and molecular orbital theory that the students had received in their lower level courses, supplemented by handouts we gave them on semiempirical and ab initio calculations, appeared to be sufficient for them to use the software with few problems. In fact, the prior experience with computational methods was very helpful when we reached this material in lecture because it made the abstract discussion of quantum chemistry more concrete. Doing the calculations on desktop PCs rather than workstations also worked well for small molecules. The CAChe PM3 and ZINDO calculations for the benzene derivatives usually took no more than a minute or two and even the longest Gaussian calculation (geometry optimization and frequency calculation for C5 at the MP2/6-31G(d) level) took only 67 minutes. We also are enthusiastic about the collaboration. Combining the students from three classes gave us a group large enough to do research-style projects. With encouragement from the instructors, most of the students were active participants in the email discussions (the moderator received more than 120 email messages from groups of students during the carbon clusters project). Some of the activities during the projects were prompted by questions from the students. We also found that group problem solving was effective when the students were in the uncharted waters of a research project involving computational methods that they were just learning. Although individual answers sometimes were incomplete or wrong, group consensus eventually led to the correct ones. To help with this process and to avoid a few members’ dominating the discussion, the moderator, who received the messages, often held them for a day or so before distributing them to the group. This gave more students a chance to participate and allowed differences of opinion to develop. Finally, we were pleased that some form of camaraderie developed among the students from the three schools, few of whom knew each other before the project began. At the request of the students, we held a “Carbon Clusters Convention” at the end of the semester so that they could get together, eat pizza, and meet their co-workers.
Extensive background information with instructions and questions for students is available as supplementary material for this article in this issue of JCE Online.
Acknowledgments Partial support for this work was provided by the National Science Foundation’s Division of Undergraduate Education through grant DUE-9452453 and by a CAChe Scientific Higher Education program grant. We would like to thank Theresa Zielinski for discussions that gave us the idea for our online collaboration.
Supplemental Material
Literature Cited 1. Chem. Eng. News: 1994, 72(Sep 12), 21–27; 1995, 73(May 1), 36–47; 1995, 73(Sep 4), 30–40; 1996, 74(Apr 22), 10– 17; 1996, 74(Sep 16), 30–37; 1997, 75(May 12), 30–40; 1997, 75(Oct 6), 26–32; 1998, 76(Apr 27), 31–37; 1998, 76(Sep 28), 25–43; 1999, 77(Apr 26), 24–30; 1999, 77(Sep 27), 29–37. 2. Brown, F. B. In Moore, R.; Schwenz, R., Eds. Physical Chemistry: Developing a Dynamic Curriculum; American Chemical Society: Washington, DC, 1993; p 2. 3. Quantum CAChe 3.0; Oxford Molecular Group, 13475 SW Karl Braun Dr., Beaverton, OR 97077. 4. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Gill, P. M. W.; Johnson, B. G.; Robb, M. A.; Cheeseman, J. R.; Keith, T.; Petersson, G. A.; Montgomery, J. A.; Raghavachari, K.; AlLaham, M. A.; Zakrzewski, V. G.; Ortiz, J. V.; Foresman, J. B.; Cioslowski, J.; Stefanov, B. B.; Nanayakkara, A.; Challacomb, M.; Peng, C. Y.; Ayala, P. Y.; Chen, W.; Wong, M. W.; Andres, J. L.; Replogle, E. S.; Gomperts, R.; Martin, R. L.; Fox, D. J.; Binkley, J. S.; Defrees, D. J.; Baker, J.; Stewart, J. P.; Head-Gordon, M.; Gonzalez, C.; Pople, J. A. Gaussian 94, Revision E.1; Gaussian, Inc., Pittsburgh PA, 1995. 5. Zielinski, T. J. Papers and Essays; http://www.niagara.edu/~tjz/ e_p.htm (accessed Dec 1999). 6. Nassau, K. The Physics and Chemistry of Color; Wiley: New York, 1983; p 128. 7. Whisnant, D. M. Computational Chemistry Project: Hair Dyes; http://personal.wofford.edu/~whisnantdm/hair/hair.htm (accessed Dec 1999). 8. The Merck Index; 11th ed.; Budavari, S., Ed.; Merck: Rahway, NJ, 1989; p 166. 9. Weltner, W. Jr.; Van Zee, R. J. Chem. Rev. 1989, 89, 1713. Campbell, E. E. B. Springer Series Chem. Phys. 1994, 52, 331. 10. Michalska, D.; Chojnacki, H.; Hess, B. A. Jr.; Schaad; L. J Chem. Phys. Lett. 1987, 141, 376. 11. Bernath, P. F.; Hinkle, K. H.; Keady, J. J. Science 1989, 244, 562. 12. Whisnant, D. M. Carbon Clusters Project. http:// personal.wofford. edu/~whisnantdm/carbon/cluster.htm (accessed Dec 1999). 13. Hanks, T. W. J. Chem. Educ. 1994, 71, 62. 14. Martin, J. M. L.; François, J. P.; Gijbels, R. J. Mol. Struct. 1993, 294, 21. 15. Vager, Z.; Feldman, H.; Kella, D.; Malkin, E.; Miklazky, E.; Zajfam, Z.; Naaman, R. Z. Phys. D 1993, 26, 343. 16. Van Zee, R. J.; Ferrante, R. F.; Zeringue, K. J.; Weltner, W. Jr. J. Chem. Phys. 1987, 86, 5212. 17. Foresman, J. B.; Frisch, Æ. Exploring Chemistry with Electronic Structure Methods, 2nd ed.; Gaussian: Pittsburgh, PA, 1995; p 64.
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