Using Computational Methods To Teach Chemical Principles

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Using Computational Methods To Teach Chemical Principles: Overview Downloaded via 5.62.152.96 on July 26, 2019 at 02:43:53 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Alexander Grushow*,1 and Melissa S. Reeves*,2 1Department of Chemistry, Biochemistry & Physics, Rider University,

Lawrenceville, New Jersey 08648, United States

2Department of Chemistry, Tuskegee University,

Tuskegee, Alabama 36088, United States *E-mail: [email protected] (A.G.). *E-mail: [email protected] (M.S.R.).

While computational chemistry methods are usually a research topic of their own, even in the undergraduate curriculum, many methods are becoming mainstream and can be used to appropriately compute chemical parameters that are not easily measured in the undergraduate laboratory. These calculations can be used to help students explore and understand chemical principles and properties. Visualization and animation of structures and properties are also aids in students’ exploration of chemistry. The ubiquity of personal computing devices capable of running calculations and the user-friendliness of software to fully optimize small and medium molecules using graphical interfaces and drop-down control menus has made it possible to readily use computational chemistry tools in most chemistry courses in the undergraduate curriculum. This book will focus on the use of computational chemistry as a tool in the classroom and laboratory to teach chemical principles.

Introduction The chapters in this book are the result of the growing ubiquity of theoretical and computational methods in all facets of chemistry education. For better or worse, the days of hand calculating solutions to Schrödinger equations are long gone. The ability to use a computer to solve thousands of equations in the blink of an eye makes it possible to pursue computations that generate meaningful results in a very short time. Whether those computations are quantum mechanical, statistical mechanics or examining molecular dynamics, the even greater power of modern computational chemistry is the ability to visualize the results of these calculations in ways that provide real chemical insight to both experts and novices. It is the latter group that the authors within this book serve. © 2019 American Chemical Society

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

Herein are described many different uses of computers, from high level quantum mechanical calculations, through molecular dynamics simulations to the use of mathematical engines to model chemical systems. All of this computing power however is targeted at teaching students about chemistry. Along the way students will likely learn some other computer-based techniques, but the goal is to learn about chemistry. The symposium that resulted in this book was held at the 254th ACS National Meeting held in Washington, DC. Many of the authors in this book presented talks during that symposium, which also was highlighted by an extended afternoon break for participants to go outside and witness the solar eclipse of 2017. Other chapters represent the work of authors who could not participate in the symposium, but provide valuable insight into ways computational chemistry can be used to teach chemical principles. We are emphasizing the idea that the focus can now be on learning chemistry and not on the theoretical methods themselves. While we are not diminishing the importance of the theoretical background, we wanted to document the myriad of ways to teach chemistry using computational methods. The objective of this book is to provide the reader with examples of the use of computational methods in the classroom and laboratory in various institutional settings. While the use of computational methods has been developing for years, we felt that the work of the authors was important to present even though we have not had the opportunity to systematically assess the outcomes of these innovations. We expect that research will be done to explore the effectiveness of computational methods in the teaching of chemical concepts as computational methods become more mainstream. Wherever possible we have asked authors to comment on their experiences, challenges, and successes, including student feedback when available, but our primary focus has been to publicize what various instructors have done to promote the use of computational methods in the teaching of chemistry. In the meantime, we hope that you will find some use in learning about the current innovations and about the successes and challenges that the authors have experienced in bringing computational methods to bear in the teaching of chemistry.

History of Computational in Chemistry in Our Classes When we first started our teaching careers, desktop computers were just starting to be regularly used to perform ab initio or semi-empirical quantum mechanical calculations. More often than not however, the software was limited to a single or small number of available licenses. And examination of anything more than a few heavy atoms took longer than the typical undergraduate student attention span. As a result, these packages were usually used in research situations or on a very limited basis in the undergraduate curriculum to provide a single example of computational methods. Molecular mechanics and dynamics could also be performed on small systems using a desktop computer, but the limitation with this type of computation was that commercial packages were often costly and the low cost (and free) applications often did not come with a useful graphical interface. While we are both physical chemists and had used computational resources in our own research, bringing it into the classroom was fraught with many difficulties. Most of the time students needed to learn new computer constructs, such as coding and command-line instructions. While we did feel that these kinds of experiences were important for our undergraduates to engage in, because of the value of computational methods used in professional research situations in chemistry, many of the early exercises expended much more effort in computer programming and less time thinking about the chemistry questions.

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As indicated by a number of authors in this book, there has been a great development of computational tools to both quickly calculate and easily visualize results of calculations on many types of chemical systems. These developments have changed our questions from “how do I get students to learn how to use computational software?” to “what chemical questions can we explore using this relatively easy tool?” Within this book, you will find chapters that explore chemistry questions that utilize tools such as high-level quantum computational chemistry, molecular dynamics simulations, and computational engines to visualize complex mathematical functions used in physical chemistry. The material, however is not just for the physical chemist, as we have contributions from organic chemistry and general chemistry as well.

Selected Landmarks in Computational Chemistry Education Computational chemistry has been part of the undergraduate curriculum for decades (1), but has stubbornly remained in isolated pockets of particular departments. Despite the optimistic J. Chem. Ed. editorial “Computational Chemistry for the Masses (2)” from 1996, computational chemistry did not spread to the masses. As recently as 2015, Fortenberry, et al., argued that computational chemistry had still not entered the standard undergraduate chemistry curriculum (3) [emphasis added]. The same argument has been made by Johnson and Engel (4). Most purveyors of computational chemistry have made sincere efforts to woo the undergraduate education market with specialized packages, books (5–7), and even workshops for professors. One large barrier has been the expense of equipping a computer classroom and purchasing the license to a suite of software; a second barrier is the regular maintenance and upgrades needed for hardware and software. Finally, the professor running the course has to have a plan, curricular materials, and the expertise to use the computational tools within the curriculum. A coarse timeline of “landmark” events in the last 25 years of computational chemistry education here begins in 1993 (see Figure 1) with the publication of the Schwenz and Moore Physical Chemistry: Developing a Dynamic Curriculum (8). Three of the 31 chapters were computationally oriented, covering ab initio calculations (9), Hückel calculations (10), and using Monte Carlo calculations to simulate kinetic data (11). That same year, a review by Casanova covered molecular modeling in education up to that date (1). Coincidentally, 1993 was also the debut of Mosaic, the first web browser for general users. The 90s saw a rise in the graphical user interface (GUI) and software designed for the desktop computer. For example, Gaussian was released for the Windows-based PCs in 1994, and Spartan for Mac (1994) and Windows (1995) were released. Gaussview (the Gaussian GUI) was first available in 1997. Many efforts were made by commercial software companies to produce educational materials in this period (5–7). Gaussview/Gaussian and Spartan remain highly popular today. In 1998, a paper detailed the “Integration of Computational Chemistry in the Chemistry Curriculum (12)” at UNC Wilmington; computational chemistry was incorporated in six courses there, including Organic 1 and 2. Other papers detailed single courses (13) or single experiments (14–16) utilizing computational chemistry. There was a sea change in 2000: the initial release of WebMO (17, 18) and the rise of webbased computational chemistry. Growth was burgeoning in educational use as well in 2001 there was both an ACS Symposium “Teaching Chemistry in the New Century: Physical Chemistry (19)” which listed 6 computational presentations out of 18, including talks on molecular dynamics and using symbolic math programs and also a full day symposium at the fall ACS meeting entitled “Computational Chemistry in the Undergraduate Curriculum.” These were emblematic of the 3 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

incursions of computational chemistry into education. These incursions continued with a number of symposia, usually about physical chemistry education, that would include aspects of computational chemistry. While computational chemistry is often used as a synonym for ab initio electronic structure calculations, many of the symposia included a broader view, as we have done in this book, to include other types of computation such as molecular mechanics and dynamics, kinetics simulators and the use of symbolic math programs.

Figure 1. Timeline of landmark events in computational chemistry education. The top row is about technology, the middle row about software, and the bottom row is published works. The rise of the smartphone, tablet and low cost laptop may have finally broken the cost barrier. We will date this landmark at the advent of the iPhone in 2007. Essentially all college students now arrive at the classroom with a 1990s supercomputer in their back pocket; they are pre-equipped to do high level computation and visualization (20). After hardware costs, the next largest barrier (for the masses) is the purchase and maintenance of software. Freely distributed packages have long been available, such as PSI4 (21, 22) and GAMESS (23, 24) for electronic structure calculations and TINKER (25, 26) for molecular dynamics. However, the technical issues with downloading packages, installing them, and maintaining them are nonneglible. These barriers, too, may be falling with freely available software packages such as WebMO as a web client-based front end to freely distributed packages such as GAMESS and PSI4 (and the WebMO app (27, 28) as the front end to the front end). There are also freely accessible web servers such as Chem Compute (29). This brings us to the present time and the final barrier, which is that professors interested in using the computational tools may be uncertain how to use them in the classroom or lab because of a lack of training. At some point, we envision that there will be a computational experiment in every lab manual from General Chemistry on up to Physical Chemistry, but that point has not yet been reached. The chemical education literature now has a number of computational experiments, some of which have already been referenced and others which are described in other chapters in this book. PSI4 Education (3) is a recent project to build a library of freely available curricular materials. The POGIL-PCL project (30, 31) has developed and tested three guided inquiry computational experiments. Another recent ACS Symposium Series book (32) also has a couple of examples of physical chemistry experiments in computational chemistry or with computational components. Since the American Chemical Society Committee on Professional Training issued the guidelines allowing advanced courses to replace the traditional two-semester sequences of organic and physical chemistry (33) it has become possible to have a course entirely about computational chemistry as

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part of the undergraduate major. There is certainly space in the curriculum to use computational chemistry.

Balancing Theory and Application Clearly the tools are available and there is more room in the curriculum to include computational methods in a course or a lab. The question is, how to implement? As with the utilization of complex instrumentation, the use of computational methods in chemistry laboratories also raises the question of how much a student needs to understand about the inner workings of the tools they are using to learn chemistry. In the world of laboratory instrumentation, for example, there is the still open question “Does a student need to understand the nuclear relaxation phenomenon to use an NMR instrument?” Or do students need to know how to shim a magnetic field in an NMR instrument? Does a student need to fully understand population inversions and how lasers work in order use a laser-based spectrometer? There has long been an interest in examining how instruments are used in the chemistry curriculum (34) but only recently has there been some examination into how use of instrumentation in the chemistry laboratory impacts student learning (35). However, the definitive answer of how much students need to understand about those instruments has not appeared in the educational literature. On the other hand, the instrumentation technology and automation has rendered the question moot, as it is often counterproductive to get “under the hood” for many instruments. At the undergraduate level, the goal is often to provide students the experience of using instrumentation and learning how to interpret the resulting data. The advanced work of understanding how instrumentation works and its limitation is often left to advanced courses, independent research or graduate studies. The similar question in computational chemistry is, how much do students need to understand about the methods they use? In a molecular dynamics simulation do students need to understand the application of force fields from all the nuclei or molecules in the system? When performing an ab initio calculation, does a student need to understand how thousands of integrals are evaluated to generate the matrix elements that will then be manipulated to form a single iteration of a structure minimization? We think, at this point in the technological development, the answer is no (see Figure 2). The use of computational tools has permeated the practice of chemistry such that their inclusion should be as mundane as obtaining an NMR spectrum. Students can learn to recognize from experience that use of a particular deuterated solvent in NMR spectroscopy might be preferred over another, without necessarily understanding why. Similarly, students can begin to recognize that HF/ STO-3G calculations are fast, but MP2/6-31G* and B3LYP/6-311+G** will improve the energy and vibrational frequencies. As stated above, both authors have engaged students in computational methods early in our teaching careers. In those early days, we had students actively coding, developing scripts and creating their own visualizations. In large part, we did this because we had to. The tools were not available to provide students the ability to answer even simple chemical questions without some work developing the computational tools. We did have students develop scripts, learn how integrals were calculated, and port output files from a computational program to some sort of graphical output. This kind of activity could take up to an entire 3-hour lab period. Now it can be done automatically in a few minutes. By using the tools to answer a chemistry question, students can explore their chosen discipline, and if they become interested in the details of the computational methodology, they can pursue that understanding in advanced coursework or independent research.

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We feel that including computational tools to explore chemical questions should be part of our goal to teach students about chemical concepts. Yet, it is clear from some of the chapters in this volume that there is still room to discuss how to use computational methods in the classroom. It really does depend on the goals for one’s course or curriculum. We could also envision a course that is designed to teach chemistry students the tools and techniques of computer programming and coding. This would be very much a course that goes “under the hood” of computational methods. On the other hand, the amount of material to potentially teach in computational methods is expansive and would likely take more than even two semesters to do it all justice at the undergraduate level.

Figure 2. In their undergraduate teaching, the authors are weighing heavily on time for application over time spent on details of the theory. In the end, it comes down to the instructor’s preference and course goals. As you read the chapters in this book, please take some time to think about how you might implement the ideas that are described within. Some of them require deep exploration of computational methods, while others use the computational tools to develop chemical understanding without seeking to understand how the tools work. Other chapters are found in between these two extremes. The reader is cautioned to make sure they understand the requirements for utilizing the tools and methods described in each chapter before adopting a particular activity.

Overview of the Chapters The original ACS symposium in Washington, D.C. was divided roughly in half between single experiments and collections of activities or full courses. We have kept a similar division in this book. The authors were encouraged to provide their personal stories as they developed their materials in computational chemistry. As a result, many of the chapters seem conversational because they are a description the process of development of these activities. In so doing the authors also provide insight into what has worked and what has not. 6 Grushow and Reeves; Using Computational Methods To Teach Chemical Principles ACS Symposium Series; American Chemical Society: Washington, DC, 2019.

In the first half of this book, the chapters describe one or a couple of computational activities. In the chapter following this one (Chapter 2), Bruce explains how computational methods can be used to provide theoretical context and visualization of kinetic molecular theory. Chapters 3, 4, and5 provide development of multi-faceted computational experiments that provide chemical insight without performing multiple laboratory experiments, many of which would be dangerous or difficult to set-up in the undergraduate laboratory. Stocker describes a series of activities that explore the energetics for the reaction pathway for the formation of ammonia. Phillips has developed a couple of activities that stem from computations on the insertion of an argon atom into the HF molecule. This experiment has a couple of avenues for additional exploration of chemistry that would not be readily available in the undergraduate laboratory. Reeves, et al., describe an experiment for exploring computational thermochemistry on halogenated compounds, showing how useful computational chemistry can be to examine potentially toxic and hazardous compounds. This group is followed by Chapter 6 in which Whitnell and Reeves explore the process of developing and testing computational experiments within a guided inquiry framework. In Chapter 7, Perri, Akinmurele and Haynie describe the computational tools that have been made available through a browser-based platform, increasing the accessibility of high-performance computing to educators everywhere. Finally, there are two chapters that explore the use of computational chemistry to extend chemical understanding developed in the physical chemistry laboratory. Chapter 8 is a single module by Martin and Ball that extends the spectroscopic study of acetylene to a computation of tritiated acetylene what is not easily obtained in an undergraduate laboratory. The final chapter (Chapter 9) in this section by DeVore describes a couple of different computational extensions to the physical chemistry laboratory from infrared spectroscopy to the Aufbau principle. In the second half of the book, the chapters cover collections of activities or full courses. Chapter 10, by Sharma and Asirwatham, details use of computational activities in an Honors General Chemistry course. This chapter is significant for its use of computation in multiple applications and topical settings throughout that foundational semester. Several freely accessible software packages are discussed. Esselman and Hill describe in Chapter 11 the integration of ab initio calculations into Organic Chemistry lab. Their work combining wet labs with insight from computations is aimed at improving students’ rationalizations of chemical phenomena. In Chapters 12, 13, and 14, different uses of computation in the Physical Chemistry sequence are described. Singleton uses Jupyter notebooks to create “computational narratives,” which combine complex calculations with written interpretations. Tribe emphasizes student programming assignments to expand student comprehension of the inner workings of computational programs. In Chapter 14, McDonald and Hagan detail using MATLAB assignments throughout Physical Chemistry to build students’ computational thinking and expertise. In Chapter 15, Grushow details a standalone laboratory course on teaching chemistry with computational chemistry intended to follow a course on the fundamentals of Physical Chemistry. The final two chapters have discussions of activities which cover a span of courses. Kholod and Kosenkov (Chapter 16) discuss using computational chemistry to add research experiences in the curriculum to a variety of levels of courses. Lastly, Price (Chapter 17) has a plan to unify multiple courses (as well as de-compartmentalize student thinking) with a study of a single unifying chemical concept: the hydrogen bond.

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Conclusion The use of computational chemistry to explore chemistry through visualization and quantification of difficult or impossible to measure properties makes it invaluable as a teaching tool. The chapters in this book provide some interesting ideas and practical insight for the instructor who wants to include more computational and theoretical lessons into their chemistry curriculum. The smart phone combined with web interfaces have brought us to a time when “bring your own device” is feasible. We have not reached the stage where every lab manual (beginning in General Chemistry) includes a computational experiment, but we are heading in that direction. The next key step will be to make computational methods more accessible for instructors who have not been previously trained in using them for chemistry instruction.

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