Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
This program is aimed at involving undergraduate students in active research under the supervision of faculty who are dedicated researchers and mentors at the CCS, an interdisciplinary computational research center at MSU.
The major concentration will be in computational methods and their applications in materials science. The objectives are to provide the undergraduate students with meaningful research experience, to show them the enjoyment of doing research, and to encourage them to pursue advanced degrees in mathematical and physical sciences. In addition to gaining experience in using computational simulation and data analysis tools, students will gain knowledge in using high-performance computing. At the end of the program the participants will prepare written reports and give presentations of their research. The program will be enhanced with various scientific, cultural and social activities.
More about Research Experiences for Undergraduates:
Items from 2025
Anomaly Detection in Material Images Using Scan Statistics, Kaitlyn Anderson, Asanka Duwage, and Tung-Lung Wu
Developing a Computational Pipeline for Microstructure-based Modelling with ExaCA and EVPFFT, Lizzy Beall, Eric Collins, and Jacob Moore
Computational Investigation of Substituent Effects on Zirconium Pincer Complexes via Density Functional Theory Methods, Anna Constable, Garrett M. Wells, Samuel D. Juarez Escamilla, Thedford K. Hollis, and Charles Edwin Webster
Virtual screening of peptides that can prevent insulin aggregation, Thanh Tien Dao, Bidisha Sengupta, and Steven Gwaltney
Efficient Image Denoising Models with Anderson Acceleration using Finite Difference Methods, Amanda E. Diegel, Spence Hanegan, Hyeona Lim, and Hoang Tran
Finite Element Methods with Anderson Acceleration and its Application to Image Denoising, Amanda E. Diegel, Spence Hanegan, Hyeona Lim, and Hoang Tran
Investigating the Defect Behavior and Electronic Properties of Formamidinium Lead Bromide Perovskite through Machine Learned Interatomic Potentials, Rachel Lee, John Michael Lane, Woodrow Wilson, and Neeraj Rai
Developing a Neural Network for Prediction of Interatomic Energies in Iron-Manganese Alloys, Robert D. H. Race, Doyl Dickel, and Hala Ben Messaoud
Neural Network Prediction of Titanium-Aluminum Mechanical Properties, Carter Reed and Kip Barrett
Absence of Superconductivity in the lightly doped Hubbard model, Jodie Roberts and Rudolf Torsten Clay
Items from 2024
New Weighting Parameters for Non-Local Means based Denoising Algorithm, Ely Carroll and Hyeona Lim
Validation and Runtime Improvements in Neural Networks for Interatomic Potentials via Automatic Fingerprint Selection, Spencer Evans-Cole, Kip Barrett, and Doyl Dickel
Continuous Data Assimilation for Two-Phase Flow, Mary H. Graveman and Amanda E. Diegel
Developing Machine Learned Interatomic Potentials for Hydrogen Dissociation over Molybdenum Phosphide, Jeremy Lugo, John Michael Lane, Woodrow N. Wilson, and Neeraj Rai
Repulsive Coulomb interactions enhance Superconductivity Selectively at Density 0.5 per site, Jeremy Padvorac and Rudolf Torsten Clay
A docking Analysis of reactivators for sarin-inhibited acetylcholinestrase can aid in screening candidate compounds, Ryan Pirger and Steven Gwaltney
Development of a Simulation Tool to Study Secondary Electron Emission in Copper Coated with Graphene, George Vassilakopoulos and Eric Collins
Alkylidyne Molybdenum formation from Lithiated Transmetalation: A Computational Insight, Niles Wahlin, Nghia Le, and Charles Edwin Webster
