The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public, but we request that all who plan to attend register via Sessions in advance. Planned sessions will be canceled if no one signs up to present.

Aniso-ChIMES: Shape- and Symmetry-Adapted Machine-Learned Interaction Models for Anisotropic Nanoparticles

Colloidal nanoparticles can self-assemble into functional materials for applications in sensing, catalysis, and photonics. However, accurately modeling their interactions is challenging because these interactions depend on particle shape, surface chemistry, and relative orientation. My research addresses this challenge by developing Aniso-ChIMES, a coarse-grained, machine-learned interaction model that extends the existing ChIMES framework with shape- and symmetry-aware features. By incorporating particle geometry into the model and training process, we improve sampling and fitting efficiency while maintaining accuracy. We further extend the model to support molecular dynamics simulations, allowing us to validate its predictions and investigate the structural and orientational ordering of representative nanoparticle assemblies. This framework provides a generalizable approach for understanding nanoparticle self-assembly and guiding the design of functional materials.

Melody Zhang (Chemical Engineering and Scientific Computing)

Melody Y. Zhang is a Ph.D. candidate in Chemical Engineering at the University of Michigan, co-advised by Professors Rebecca K. Lindsey and Sharon C. Glotzer. Her research focuses on developing physics-informed machine-learning models that capture chemical diversity and anisotropic nanoparticle interactions, enabling studies of self-assembly and the inverse design of functional nanomaterials.

If you have any questions, please email [email protected].