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Self-Assembly of Simplified Molecular Models of Advanced Energy Materials

Faculty Advisor: Dr. Eric Jankowski

Engineering the equilibrium morphology of complex macromolecules is important for the manufacturing of new solar materials, quantum computing materials, and piezoelectric materials. Molecular simulations can help predict how morphology depends on chemistry and processing conditions, but simplified models are often needed to access experimentally-relevant timescales. In this project, we apply coarse-grained models that have been tailored with machine-learning techniques to predict the equilibrium phases of families of macromolecules with applications in organic electronics.

Role of Participant(s):

Participant(s) will collaborate with teammates to learn and apply scientific software-development skills in service of molecular simulation workflows.  They will perform equilibrium molecular simulations and quantify structural details.  Prior programming skills will not be required, although familiarity with python and command-line interfaces would be advantageous.  Opportunities to use machine learning techniques, present work at meetings and conferences, and contribute to journal publications will be emphasized.

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