(459a) Introduction to Computational Research for First-Year Chemical Engineering Students

The age of high performance computing and evolution of data science tools have increased the use of computational methods as primary research tools. However, participation by undergraduates in computational research still lags participation in experimental research due to barriers (real or perceived) associated with their computational skills, which generally receive less attention than laboratory skills in first year chemistry/chemical engineering curricula.^1-2 A substantial effort is therefore required to train students in relevant computational techniques (in addition to technical training on research), limiting the time students spend on “actual research”.

In this presentation, we will discuss a semester-long course (1 credit, expected total commitment of 3 hours/week, satisfactory/fail grading) designed for first-year engineering students that introduces computational research in a low-pressure, low-risk environment. Our course is developed in the context of applying density functional theory (DFT) calculations to problems in heterogeneous catalysis and materials science, though we anticipate applicability to a number of other computational research fields. The primary objective of the course is to develop confidence in students who may have no background knowledge to explore potential interests in computational research. The course structure adapts over the course of the semester as students develop increasing independence. Initially, the instructor and students meet for 1 hour each week to learn tasks such as navigating a command-line Linux environment and fundamental concepts of computational research in the related field. Beginning in the fourth week, these meetings are shortened to 30 minutes, during which the students learn about how to set up and submit simulations. Students spend the balance of their 3-hour weekly commitment working on homework assignments and handouts that apply the concepts discussed in the meeting in new systems. In roughly the sixth week and beyond, weekly meeting time is reduced to 15-20 minutes, in which the team meets to discuss issues faced in the past week, share basic data analysis, and identify goals for the upcoming week(s). The semester concludes with collaboration on a shared spreadsheet to combine, organize, and analyze data on the given problem. A minimal emphasis is placed on report writing (although a high emphasis is placed on overall professionalism); the primary goal is to obtain familiarity with relevant techniques. Overall, the curriculum emphasizes independence in completing research tasks while developing team-based learning strategies for analysis. Monthly evaluations are carried out to discuss: (a) perceived workload, (b) students’ motivation, (c) perceived benefits/drawbacks of the designed course structure, and (d) students’ learning. Feedback is shared anonymously with the instructor to incorporate the students’ suggestions for improving the teaching-learning process. In its first two semesters of implementation, all three students who successfully completed the course have opted to continue research in the group for elective credit (3 credits, letter grade).

References:

(1) Alford, R. F. et al. PLOS Comp. Bio. 13, e1005837 (2017).

(2) Ordóñez, P et al. 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), Portland, OR, USA, pp. 241-242 (2020).