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Chem Compute Science Gateway: An Online Computational Chemistry Tool Downloaded via AUBURN UNIV on August 20, 2019 at 22:07:11 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Mark J. Perri,* Mary Akinmurele, and Matthew Haynie Department of Chemistry, Sonoma State University, 1801 E. Cotati Avenue, Rohnert Park, California 94928, United States *E-mail: [email protected].

The Chem Compute Science Gateway (chemcompute.org) is a free website where undergraduate chemistry students can easily setup a job, submit it to an XSEDE supercomputer, and visualize the output. Our website has several advantages over installing software packages locally. First, it is free to use and available anywhere on any device; it can be accessed by students off campus so that they can start a long job before class, leaving class time for data analysis. Second, jobs are run on XSEDE servers, enabling great computational power and long run times. Third, a web-interface is familiar to students, helping to put them more at ease with a difficult and feared subject. Fourth, several lab exercises are built into the website, eliminating the need for faculty to find or create computational labs. This free resource eliminates many barriers to computational chemistry such as cost of software, hardware, and faculty workload. This chapter describes the author’s early attempts and failures at using free computational packages in class, the web-based science gateway that was built to enable graphically-based access to these packages, and the successes encountered after using the gateway in classes at a primarily undergraduate institution.

Introduction History Years ago while teaching Physical Chemistry Lab for the first time I wanted our students to perform computational chemistry experiments. Our department had only one license for Spartan (Wavefunction, Inc.), a commercial package with an easy to use graphical interface (and no money to purchase more licenses). I tried having 12 students huddle around a single computer and perform some calculations. It went about as well as one would expect: one student did all the work and the rest worked on their social media accounts on their phones. I decided that I would find a way for all students to work on their own computers. Next year, (with much grumbling from IT) we © 2019 American Chemical Society

Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

installed GAMESS (1), a free computational package, two other helper programs (Avogadro (2, 3) and MacMolPlt (4)), and some scripts that I wrote to help out with the process. Despite belonging to the “Digital Generation”, the students completely lacked command line skills and patience. Students complained and were mostly unable to correctly format an input file or spell file names (and extensions) correctly. After frantically running around the room fixing spaces, dollar signs, and DOS commands, I started working on a website to automate the process. The Chem Compute Science Gateway was first released locally in October, 2013 (5). Originally named GAMESS Web, the service was available only to Sonoma State University Undergraduates because of our University’s draconian firewall rules. This preliminary version of the website still required students to generate text input files and to download and view the output files themselves, but it handled job submission for them by assigning work to four desktops in my lab that I networked together using the Sun Grid Engine (SGE) Scheduler (6). I used the site in my Physical Chemistry class (second semester Quantum Mechanics lecture) with moderate success and found that even a primitive web interface was easier for undergraduate chemistry students to use than directly running GAMESS on the command line. I thought that other universities might benefit from this free tool. In January 2014 I convinced our IT department to allocate a small virtual machine (VM) to serve the website and it was then accessible to the outside world. The user base increased slowly, but eventually outgrew the computational capacity of the four desktops that I had networked together. In 2016, I turned to the NSF XSEDE Supercomputer Network (7) for a startup allocation on Comet, a supercomputer at the San Diego Supercomputing Center, and received 25,000 CPU-Hours (Service Units / SU) for calculations. The site had gotten so popular that large universities were using it and the small VM that Sonoma State provided to host the website was overloaded. The following year, I was allocated 25,000 CPU-Hours on Comet and another XSEDE supercomputer, Bridges (Pittsburgh Supercomputing Center), for computations. Importantly, I was also awarded 150,000 CPU-Hours on Jetstream (8) (Indiana University Pervasive Technology Institute) to host the website. Jetstream is an XSEDE cloud-based supercomputer, where users launch their own VMs for calculations or web hosting. Our current web hosting VMs on Jetstream are large enough to accommodate multiple institutions with hundreds of students simultaneously performing calculations. Barriers to Computational Chemistry While attempting to mount computational chemistry labs on a budget, I ran into three barriers. 1. Software: The cost of easy to use computational software can run thousands of dollars. Free software such as GAMESS (1) is available, but requires either command line knowledge to create an input file and run a job or installing third-party tools such as Avogadro (2, 3) and GamessQ (9). Viewing output files requires a third party program such as wxMacMolPlt (4). I have found that installing all these tools is just too burdensome and their use confuses the students. With Chem Compute, input files are created through a graphical user interface, run via batch scheduler (SLURM) (10), and output files are visualized using JSmol (11) integration. 2. Hardware: I originally tried running GAMESS locally in our University’s computer lab. Short jobs ran fine, but longer jobs are not possible with local resources; when one’s computer lab time is up, the next class comes in. At the end of my computational labs I require students to perform a calculation on a molecule of their choosing. Many of the 80 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

students choose to calculate IR or UV-Vis spectroscopy on a molecule for their research. These jobs typically take several hours to run and cannot be finished in a lab period. Any jobs beyond ~ 10 atoms require some dedicated server hardware. Our University and many like it do not have a University Cluster to use. Chem Compute makes use of the NSF XSEDE Supercomputer Network (7) (currently Comet, Bridges, and Jetstream) to run calculations using 1 core, 6 cores, or a full node (24 or 28 cores depending on the system). Jobs can run for as long as 48 hours, giving students plenty of time to do meaningful calculations. The web interface is accessible from anywhere, so students do not need to be in the computer lab to start a job or to view the output. 3. Experiments: Part of the barrier to computational chemistry is finding experiments / simulations to use in class. Many published experiments exist for General Chemistry (12–14), Organic Chemistry (15–20), and Physical Chemistry (21–26), but they usually require commercial software, and molecular dynamics experiments are not frequently published. If one has access to a different software package (or possibly even a different version) then the instructions require translation. I have spoken to many faculty who end up writing their own unpublished computational labs because the process is so daunting. This makes it difficult for departments without computational expertise to implement computational labs. Chem Compute contains several experiments that are ready to use. By clicking on an experiment a student is presented with background theory, a prelab (if applicable), and instructions overlaid on the web interface as they are doing the experiment.

Chem Compute Capabilities and Features In order to overcome the barriers encountered, I created the Chem Compute website. Chem Compute hosts two packages: GAMESS (1), an electronic structure software package, and TINKER (27), a molecular dynamics (MD) package. Anyone in the world can freely access the site and perform calculations on XSEDE (7) supercomputers. Guest users can run jobs for 1 hour on 1 core. Users can register an account on the site or login with their instutiton’s (or Google’s) single sign-on credentials through CILogon (28). Registered users get access to longer running / more powerful systems (up to 48 hours on 24 or 28 cores) and a dashboard which lists all their past jobs. To simplify registration for classes, faculty can register just one username / password to share with their students (though if no jobs run for more than one hour, registration is not necessary). Chem Compute provides all the services that students need to perform computational experiments: The experiment itself, creation of the input file, job submission, output visualization, and a log of past jobs. Experiments Chem Compute currently has nine ready to use quantum mechanics experiments using GAMESS, such as molecular orbitals and bonding and calculation of a potential energy surface. Collaborations introduced through writing this book chapter allowed us to add computational experiments exploring transition state theory applied to ammonia formation (K.M. Stocker) and an exploration of the ligand-free Suzuki-Miyaura coupling (17) (B. Esselman). Others have been added with permission from the original authors, and the rest were added by our group. In all, these computational exercises cover Introductory Chemistry for Non-Majors, General Chemistry (12–14), Organic Chemistry (17), and Physical Chemistry. Faculty are always encouraged to submit 81 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

their own experiments for inclusion on the website. These experiments include background theory, a prelab (where appropriate), and on-screen instructions and questions (Figure 1). These experiments remove the barrier of faculty having to develop their own labs or finding labs to run and adapting them to their particular software package. At present there is currently one experiment using molecular dynamics (TINKER), “What factors govern the escapability of a molecule from a liquid?” by Reeves and Whitnell (26). The MD community is actively encouraged to submit more experiments to further enhance our offering.

Figure 1. Instructions for investigating H2 (measuring the bond length).

Input File Generation Chem Compute employs an easy to use graphical interface for input file generation (Figure 2). GAMESS requires a molecule’s 3D coordinates be entered according to a strict format, and a working knowledge of numerous keywords to successfully setup a calculation. Chem Compute uses the interface from Angel Herráez’ DIY molecules site (29, 30) to setup calculations. Students draw their molecule in 2D using the Javascript Molecule Editor (JSME) (31) or can look up a molecule by name in a number of databases. JSmol (11), an all-purpose molecular structure viewer, will convert that 2D representation into 3D coordinates, add hydrogens as needed, and perform a quick molecular-mechanics optimization. SYVA (32), a program to analyze symmetry of molecules based on vector algebra, is used to determine the molecule’s point group, symmetrize the coordinates, and to determine how to align the molecule in the manner that GAMESS requires for its runs. The TINKER graphical interface is in a preliminary state. Students can choose from simulation boxes that are pre-setup for the molecular escapability lab (isopentane, neopentane, and n-pentane, either alone or in an ensemble). Students can vary temperature, timestep, and simulation length. In the future, the interface will allow students to create simulation boxes with molecules of their choosing. 82 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

Figure 2. Creating a molecule to investigate. Molecules can be loaded from a database, loaded from a previous run, copied from the clipboard, or drawn using JSME (31) (Javascript Molecule Editor). Job Submission Once the particulars of a simulation are setup, Chem Compute submits the job to the batch scheduler on an XSEDE cluster. Short jobs (less than 6 hours) are submitted using 1 core on Jetstream (located at Indiana University). Jetstream is an elastic cloud cluster. It consists of the web server for the site and one compute node for jobs. If the base compute node becomes fully allocated the Chem Compute site will launch additional nodes to meet demand and delete those nodes when the demand is over. This burst capability is used because the peak usage is much higher than the average usage. Most General and Physical Chemistry classes tend to use the website in November and April, coinciding with the topics of quantum mechanics and molecular orbitals. This elastic ability allows the site to handle peak loads efficiently. Longer jobs (up to 48 hours on 24 or 28 cores) are submitted to Bridges (Pittsburg Supercomputing Center) or Comet (San Diego Supercomputing Center). Instructors can quickly setup multiple runs by uploading a .csv file containing several molecules and settings. This batch job functionality can be used to explore the effect of different basis sets and settings on energy values (in order to find which more closely matches experiment) or to just quickly run several calculations on various molecules without having to submit calculations one at a time. Output Visualization Students can view the progress of their job on the status page. The output updates every ten seconds, displaying a summary of their job settings, the last few lines of the output file, and the current geometry of the molecule (Figure 3). I have found that students do not like to wait to see something happen, so it’s important to show them the intermediate output so that they can see the 83 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

job is running. Without intermediate output students have a tendency to resubmit jobs that they think are “stuck” or “not working”, leading to excessive job submissions. Students also have difficulty decoding and fixing error messages, which adds to their frustration. The Chem Compute site parses the output file for common error messages and attempts to translate them to a more readable form for the student.

Figure 3. Intermediate output displaying job status, the last few lines of the output file, and the current geometry visualized with JSmol. When a job is finished the output file is downloaded into JSmol (11) for visualization. JSmol can display molecular orbitals (Figure 4), vibrational modes, the dipole moment, the electrostatic potential, and can animate the molecule to show how its geometry changed during optimization (Figure 5). If IR or UV-Vis transitions are calculated, the site will graph them using a default line width and the calculated transition energies and oscillator strengths (Figure 6). Thermodynamic values (E, H, G, CV, CP, and S) are displayed along with the zero point energy, rotational constants, and partition function values. 84 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

Molecular Dynamics (TINKER) runs are visualized using ChemDoodle (33) and Google Charts (Figure 7) by displaying a movie of the simulation trajectory along with the energy of the system as a function of time. The output file is a zip archive with the trajectory file (for visualization in VMD (34)) and an energies.csv file that contains energies extracted as a function of time for further analysis by the student. The energies.csv file was created using scripts from R. Whitnell.

Figure 4. One of the π-molecular orbitals of benzene visualized with JSmol. Students refer to this orbital as “the hamburger orbital”.

Figure 5. Visualization Options available on Chem Compute.

85 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

Figure 6. Calculated IR spectrum of benzene (cm-1) using a default linewidth. The y-axis is scaled from the oscillator strength. Screen Capture from Chem Compute.

Figure 7. TINKER output containing a movie of the simulation trajectory visualized with ChemDoodle and a plot of energy as a function of time using Google Charts. 86 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

Record of Past Jobs If a student is logged in they can view their dashboard, which shows a record of their past jobs and job settings. Often a student will forget to record some data, e.g. the units of energy, or otherwise need to look back at past jobs. If a student is running multiple jobs they can quickly see whether a job has finished. If the instructor shares one username / password with the class then the instructor can use the dashboard to monitor the class’ progress. When a student needs help with an error, the instructor can quickly locate their job on the dashboard and view the error. The dashboard also shows how many CPU-hours (service units, SU) the account has used and includes a link to request more CPU-hours and access to more cores / more powerful computing nodes.

Successes Using Chem Compute in Class Students Completed the Assignment When my class used GAMESS in command line mode with an input file setup through Avogadro, a significant number of students were not able to complete some of the calculations. Errors in positioning molecules (especially diatomics) and running command line scripts dominated class time, and I had to distribute output files for students to analyze. When I switched to using Chem Compute with a web-based interface, all students were able to correctly submit jobs and view their output. I feel that this is definitely a success because it reduced frustration, which is typically very high in a Physical Chemistry class. Student Evaluations Increased While using the Chem Compute website in class, students have made very positive comments, both informally, and through end-of-semester student evaluations. Informal comments include “I wish we used this in organic – it would have explained so much.”, “I liked the ability to see the orbitals and move the molecules around”, and “Can I use this in my research?” Importantly, student’s enthusiasm was recorded in their student evaluations, where scores increased on questions including: “In this course, my instructor enables me to participate actively in learning” and “My instructor stimulates interest in the course“ (Table 1). Table 1. Increase in student evaluation responses after using Chem Compute in class. Maximum score is 5.0 Evaluation Question

Before Chem Compute

After Chem Compute

In this course, my instructor enables me to participate actively in learning

4.6

4.6

5.0

5.0

My instructor stimulates interest in the course

4.2

4.4

4.9

4.9

Classes (left to right): 2012 Spring P-Chem (N=20), 2013 Spring P-Chem (N=29), 2015 Spring P-Chem (N=14), 2017 Summer Gen-Chem (N=7).

Identification and Corrections of Common Misconceptions and Knowledge Gaps Assigning computational chemistry exercises has introduced me to a number of misconceptions concerning Physical Chemistry. It is amazing what one takes for granted as common knowledge as a professor. “Basic” or “easy” exercises that look at fundamentals, like orbitals and bonding, are often surprisingly effective at exposing misconceptions that can be fatal to a student’s understanding. I have 87 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

found that for every student who voices a misconception, there are several students who are too afraid to speak up. Some General Chemistry students thought that the subshell d was the same as the D used for referring to the dimension of space. Students were comfortable with viewing a 3d atomic orbital, but questioned whether a 4d orbital should be viewable on a computer, because a 4-dimensional object is not possible to visualize. Now I make an extra effort to distinguish between a 3d orbital and a 3-D object. Nodes are a fundamental part of quantum mechanics, but many students don’t have the mathematical background to grasp this concept. When teaching Physical Chemistry, one can write the equation for a pz-orbital (35) on the board,

but getting students to recognize the angular node produced by the cos θ term is not easy. I have found that combining the mathematical equation with a visual exploration of the atomic orbitals (Figure 8, produced by calculating the single-point energy of neon) is useful to even Physical Chemistry students. I used to think that atomic orbitals, covered in General Chemistry, did not need much explanation in Physical Chemistry, because surely the students had a great deal of experience with them. I have since found that something as simple as comparing the equation with the visualization of an orbital helps fill in gaps in a student’s understanding.

Figure 8. Neon p-orbital visualized with JSmol. Students must understand spin to correctly setup computational chemistry jobs. I have repeatedly observed students running jobs on atoms such as fluorine as a singlet species (the default option). When students get an error they usually resubmit the job. After two or three tries at this they either ask for help or read the text on the screen, “You specified a singlet spin (all paired electrons), but you have an odd number of electrons”. Spin is such a fundamental concept, especially in optical transitions, that it is worthwhile to have students struggle a bit on this, even in General Chemistry. Students are surprised to learn that although a calculation may succeed, it may not give the desired answer. For example, when asked to calculate the ground state energy of B2, students will often accept the default option of singlet spin. However, this is not the ground state as the energy is 25.9 kcal / mol higher than the triplet state when using B3LYP/6-31G*. 88 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

Constructive and deconstructive interference is the basis for bond formation, which itself is the basis of chemistry. I have found that writing:

helps students correctly solve math problems, but does not offer much help in understanding bonding. However, when students visualize the bonding and anti-bonding orbitals in H2 (Figure 9), they internalize constructive and deconstructive interference. One takeaway from my experiences teaching Physical Chemistry is that students need both a qualitative and quantitative understanding of the subject. A thorough experience visualizing the output of computational jobs boosts a student’s qualitative understanding of chemistry.

Figure 9. Antibonding (left) and Bonding (right) orbitals of H2 used to teach destructive and constructive interference. The final misconception that I have come across is also the most important. I have had many students tell me “I’m not good at computers” and that they should not be required to use computers in chemistry lab. This lack of confidence is disastrous for a scientist in a STEM field. The confusion and frustration caused by having my students use GAMESS through the command line reinforced the students’ feelings and led to a negative lab experience. In subsequent years when I had the students use GAMESS through the Chem Compute web interface results were markedly different. Using preliminary survey data (Table 2) students reported slightly increased confidence and interest in using computers to solve chemical problems after using Chem Compute. The survey is voluntary, and low post-experiment cooperation limits the usefulness of this preliminary data. The students somewhat agree / agree that the computer assignments provided deeper insight into the class material. Table 2. Preliminary Survey Results PRE (N=117)

POST (N=27)

I feel confident using computers to solve chemical problems.

4.75

5.41

I am interested in using computers to solve chemical problems.

5.21

5.52

I feel that the computer assignments provided deeper insight into the class material. 1 Strongly Disagree … 4 Neither Agree nor Disagree … 7 Strongly Agree

89 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

5.50

Connection Between Class Work and Research At the end of my computational labs I require students to perform a calculation on a molecule of their choice. Many students use their creativity to draw large, complex molecules with the molecule editor. One student even drew C60. Calculations on these large molecules invariably take too long to finish in class, and this is a good opportunity to reintroduce the concept of semi-empirical geometry optimization followed by higher level single-point energy calculation (facilitated by the “Do More Calculations” button on the output screen). Students also have chosen to run calculations on molecules they are using in their undergraduate research. Many students are involved in synthesizing novel compounds, and they use the Chem Compute site to calculate IR or UV-Vis spectra to compare with their experimental data. This connection between research and class work is invaluable. Computational chemistry has the advantage of being able to quickly create a complex molecule and perform calculations. This simply is not feasible in a typical wet chemistry lab, where students can only use compounds provided to them from the stock room. Students from around the world have used the site for research projects. One student from a liberal arts college used the site for his undergraduate honors thesis work (36). The computational power of the XSEDE network means that students have the tools to do some reasonably heavy computational work (for free) through the Chem Compute site. Engaging with Chemical Education Students Our department does not have a Chemical Educator, but a number of our students are interested in Chemical Education Research. This project has enabled me to serve as the research advisor for a number of students (two are co-authors on this chapter) who are interested in Chemical Education. These students analyze survey results, design experiments, test experiments, and provide feedback on how the experiments can be improved. It has been a great help for me to get input from a student’s perspective, and I am happy that I can bring another facet of chemistry research to my department with this project.

Conclusion The Chem Compute Science Gateway (chemcompute.org) is a free site where students can prepare, submit, and visualize computational chemistry jobs at no cost. Instructors don’t need to worry about purchasing, compiling, installing, or maintaining software and hardware. The site includes ready-to-use experiments. Chemcompute.org has provided resources for students to submit over 60,000 jobs in classes around the world. Preliminary survey results show that students who have used the site report an increased confidence and interest in using computers to solve chemical problems.

Acknowledgments This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562 under allocation TGCDA170003. It was supported by the Science Gateways Community Institute through their extended developer support (EDS). We would like to thank Sudhakar Pamidighantam, Paul Parsons, and Christopher Watkins for help with back-end job submissions and front-end redesign and Harry Price for helpful comments that improved this chapter.

90 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

References 1.

2. 3. 4. 5. 6. 7.

8.

9. 10. 11.

12.

13. 14.

15. 16. 17.

18. 19.

Schmidt, M. W.; Baldridge, K. K.; Boatz, J. A.; Elbert, S. T.; Gordon, M. S.; Jensen, J. H.; Koseki, S.; Matsunaga, N.; Nguyen, K. A.; Su, S.; Windus, T. L.; Dupuis, M.; Montgomery, J. A. General Atomic and Molecular Electronic Structure System. J. Comput. Chem. 1993, 14 (11), 1347–1363. Avogadro: An Open-Source Molecular Builder and Visualization Tool. Version 1.1.1. Avogadro.Openmolecules.Net. Hanwell, M.; Curtis, D.; Lonie, D.; Vandermeersch, T.; Zurek, E.; Hutchison, G. Avogadro: An Advanced Semantic Chemical Editor, Visualization, and Analysis Platform. J. Cheminf. 2012, 4 (1), 17. Bode, B. M.; Gordon, M. S. Macmolplt: A Graphical User Interface for Gamess. J. Mol. Graphics Modell. 1998, 16 (3), 133–138. Perri, M. J.; Weber, S. H. Web-Based Job Submission Interface for the Gamess Computational Chemistry Program. J. Chem. Educ. 2014, 91 (12), 2206–2208. Oracle Corporation. Sun Grid Engine, 6.2u4. Towns, J.; Cockerill, T.; Dahan, M.; Foster, I.; Gaither, K.; Grimshaw, A.; Hazlewood, V.; Lathrop, S.; Lifka, D.; Peterson, G. D. Xsede: Accelerating Scientific Discovery. Comput. Sci. Eng. 2014, 16 (5), 62–74. Stewart, C. A.; Cockerill, T. M.; Foster, I.; Hancock, D.; Merchant, N.; Skidmore, E.; Stanzione, D.; Taylor, J.; Tuecke, S.; Turner, G.; Vaughn, M.; Gaffney, N. I., Jetstream: A Self-Provisioned, Scalable Science and Engineering Cloud Environment. In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure; ACM: St. Louis, Missouri, 2015; pp 1−8. Bode, B. M. Gamessq, 1.2.1; 2015. Yoo, A. B.; Jette, M. A.; Grondona, M. In Slurm: Simple Linux Utility for Resource Management; Workshop on Job Scheduling Strategies for Parallel Processing. Springer: 2003; pp −60. Hanson, R. M.; Prilusky, J.; Renjian, Z.; Nakane, T.; Sussman, J. L. Jsmol and the Next-Generation Web-Based Representation of 3d Molecular Structure as Applied to Proteopedia. Isr. J. Chem. 2013, 53 (3-4), 207–216. Ruddick, K. R.; Parrill, A. L.; Petersen, R. L. Introductory Molecular Orbital Theory: An Honors General Chemistry Computational Lab as Implemented Using Three-Dimensional Modeling Software. J. Chem. Educ. 2012, 89 (11), 1358–1363. Feller, S. E.; Dallinger, R. F.; McKinney, P. C. A Program of Computational Chemistry Exercises for the First-Semester General Chemistry Course. J. Chem. Educ. 2004, 81 (2), 283. Linenberger, K. J.; Cole, R. S.; Sarkar, S. Looking Beyond Lewis Structures: A General Chemistry Molecular Modeling Experiment Focusing on Physical Properties and Geometry. J. Chem. Educ. 2011, 88 (7), 962–965. Esselman, B. J.; Hill, N. J. Integration of Computational Chemistry into the Undergraduate Organic Chemistry Laboratory Curriculum. J. Chem. Educ. 2016, 93 (5), 932–936. Esselman, B. J.; Hill, N. J. Proper Resonance Depiction of Acylium Cation: A High-Level and Student Computational Investigation. J. Chem. Educ. 2015, 92 (4), 660–663. Hill, N. J.; Bowman, M. D.; Esselman, B. J.; Byron, S. D.; Kreitinger, J.; Leadbeater, N. E. Ligand-Free Suzuki–Miyaura Coupling Reactions Using an Inexpensive Aqueous Palladium Source: A Synthetic and Computational Exercise for the Undergraduate Organic Chemistry Laboratory. J. Chem. Educ. 2014, 91 (7), 1054–1057. Helms, E.; Arpaia, N.; Widener, M. Assigning the Nmr Spectrum of Glycidol: An Advanced Organic Chemistry Exercise. J. Chem. Educ. 2007, 84 (8), 1328–1330. Habata, Y.; Akabori, S. Teaching H-1 Nmr Spectrometry Using Computer Modeling. J. Chem. Educ. 2001, 78 (1), 121–123.

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20. Palmer, D. R. J. Integration of Computational and Preparative Techniques to Demonstrate Physical Organic Concepts in Synthetic Organic Chemistry: An Example Using Diels-Alder Reactions. J. Chem. Educ. 2004, 81 (11), 1633–1635. 21. Mecozzi, S.; West, A. P.; Dougherty, D. A. Cation−Π Interactions in Simple Aromatics: Electrostatics Provide a Predictive Tool. J. Am. Chem. Soc. 1996, 118 (9), 2307–2308. 22. Shepherd, T. D.; Fortenberry, R. C.; Kennedy, M.; Sherrill, C. D. Can We Visually Predict Binding Energies? www.psicode.org/labs.php (accessed May 21). 23. Johnson, L. E.; Engel, T. Integrating Computational Chemistry into the Physical Chemistry Curriculum. J. Chem. Educ. 2011, 88 (5), 569–573. 24. Halpern, A. M.; Ramachandran, B. R.; Glendening, E. D. The Inversion Potential of Ammonia: An Intrinsic Reaction Coordinate Calculation for Student Investigation. J. Chem. Educ. 2007, 84 (6), 1067. 25. Parnis, J. M.; Thompson, M. G. K. Modeling Stretching Modes of Common Organic Molecules with the Quantum Mechanical Harmonic Oscillator - an Undergraduate Vibrational Spectroscopy Laboratory Exercise. J. Chem. Educ. 2004, 81 (8), 1196–1198. 26. Reeves, M. R.; Whitnell, R. M., New Computational Physical Chemistry Experiments: Using Pogil Techniques with Ab Initio and Molecular Dynamics Calculations. In Addressing the Millennial Student in Undergraduate Chemistry; American Chemical Society: 2014; Vol. 1180, pp 71−90. 27. Ponder, J.; Richards, F. Tinker Molecular Modeling Package. J. Comput. Chem 1987, 8, 1016–1024. 28. Basney, J.; Fleury, T.; Gaynor, J. Cilogon: A Federated X. 509 Certification Authority for Cyberinfrastructure Logon. Concurrency and Computation: Practice and Experience 2014, 26 (13), 2225–2239. 29. Herráez, A. Do-It-Yourself Molecules: From 2d to 3d. http://biomodel.uah.es/en/DIY/ (accessed May 26, 2016). 30. Herráez, A. Diy Molecules a Web Application to Build Your Own 3d Chemical Structures; In 21st Biennial Conference on Chemical Education, Denton, TX, 2010. 31. Bienfait, B.; Ertl, P. Jsme: A Free Molecule Editor in Javascript. J. Cheminf. 2013, 5 (1), 1. 32. Gyevi-Nagy, L.; Tasi, G. Syva: A Program to Analyze Symmetry of Molecules Based on Vector Algebra. Comput. Phys. Commun. 2017, 215, 156–164. 33. Burger, M. C. Chemdoodle Web Components: Html5 Toolkit for Chemical Graphics, Interfaces, and Informatics. J. Cheminf. 2015, 7 (1), 35. 34. Humphrey, W.; Dalke, A.; Schulten, K. Vmd - Visual Molecular Dynamics. J. Mol. Graphics 1996, 14, 33–38. 35. Karplus, M.; Porter, R. N. Atoms and Molecules; WA Benjamin, 1970. 36. Thiemann, N. Using Computational Tools to Design a Moleculary Imprinted Polymer with Selectivity and High Affinity for Acetylcholine; Trinity College: Hartford, Connecticut, 2017.

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