Computational Skills Training for Undergraduate Researchers in Molecular Engineering

Volume 16, Issue 1 (March 2025), pp. 50–56

Download PDF

@article{jocse-16-1-10,
  author={Kristen Finch and Ryan Beck and Xiaosong Li and Nam Pho and Xiao Zhu},
  title={Computational Skills Training for Undergraduate Researchers in Molecular Engineering},
  journal={The Journal of Computational Science Education},
  year=2025,
  month=mar,
  volume=16,
  issue=1,
  pages={50--56},
  doi={https://doi.org/10.22369/issn.2153-4136/16/1/10}
}

In June 2024, the University of Washington's (UW) Clean Energy Institute (CEI) and Molecular Engineering and Materials Center (MEMC) in partnership with UW Research Computing (RC) prepared complimentary training for a group of 25 Research Experience for Undergraduates (REU) participants.Workshop participants had completed zero to four years of post-secondary education and came from 17 colleges and universities across eight states with 29% currently attending 2-year programs. On average, 14 students attended a given workshop. The program included four targeted workshop offerings, spanning essential skills in computational science and advanced topics: (1) Python via Jupyter, (2) Command Line Interface (CLI) and high performance computing (HPC), (3) Gaussian and Quantum Espresso, and (4) data analysis using linear and logistic regression as well as neural networks. The program's effectiveness was evaluated with a post-workshop survey. Survey results indicated most participants had little prior experience in these topics but indicated the content was relevant for their current and future aspirations. The survey showed some students agreed with statements indicating that learning objectives were met, but overall scores and open responses indicated areas for improvement. In the future, the CLI and HPC session will be converted from one to two sessions and the material in the applied Gaussian and Quantum Espresso demonstrations reduced. The program's materials are reproducible and publicly accessible, compatible with most academic HPC clusters. Our program addressed a wide range of training and education needs within computational science, emphasizing practical skills and interdisciplinary applicability.