The teaching pedagogy in higher education has been rapidly evolving in the wake of COVID-19. Laboratory classes are designed to provide a visual and tangible example of theoretical and otherwise abstract topics to aid in understanding. However, with many lectures and laboratory classes being taught through remote learning, educators have found a new way to support students through computational materials science. Computational materials science utilizes different simulation and modeling techniques, combined with theory, to explore and improve understanding of different materials science topics.
A wide variety of techniques exist, such as density functional theory (DFT), molecular dynamics (MD), phase-field method, and finite element analysis (FEA). All these methods have different applications, depending on what material, property, or product is being studied. Often, computation is used to provide insight into atomic or molecular interactions that are complex or impossible to see through traditional experimental techniques. Computational materials science is also used alongside physical experiments, helping to confirm results and identify trends.
By putting the experiment back into the virtual class, simulation can allow for the visualization of complex engineering concepts, such as using FEA to understand how a crack tip evolves during fracture. This can help support or replace physical labs covering the same mechanical property topics. Computational materials science also allows for advanced experiments and problem-solving, similar to what students would accomplish in their project-based learning courses. By incorporating computational materials science techniques into virtual courses, students can complete experiments from the comfort of their homes.
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