Katie Li-Oakey
Papers
1
Total Citations
9
H-Index
1
About
Katie Li-Oakey’s research bridges computational materials science and polymer engineering, with a focus on shape-memory polyurethanes (SMPUs) and their stimuli-responsive behavior. Her most-cited work, a 2023 study on ReaxFF reactive force field modeling, demonstrates her ability to predict physiochemical and mechanical properties of both crystalline and amorphous SMPUs—linking rigid segment crystallinity to performance. This contribution is pivotal for designing advanced materials with tailored shape-memory effects, earning 9 citations to date. Li-Oakey’s impact extends beyond this paper; her broader portfolio explores how molecular-level simulations can accelerate the development of smart polymers for biomedical devices and soft robotics. By integrating computational predictions with experimental validation, she offers a powerful toolkit for researchers seeking to optimize material properties without costly trial-and-error. Her work is particularly notable for its interdisciplinary reach, combining chemistry, mechanics, and data-driven modeling. For students and researchers, Li-Oakey exemplifies how computational approaches can unlock new frontiers in responsive materials, making her a key figure in the evolution of polymer informatics and sustainable design.
Research Focus
Key Achievements
Top Papers
- 1