Graduate Student
Email: beniam1@mit.edu

 

 

 

 

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Research interests:

Crystallization strongly influences the rheology of polymer melts during processing, but detailed coarse-grained models that capture these effects are computationally expensive. A promising approach is to augment physics-based constitutive equations with neural network corrections, trained on coarse-grained simulation data. This strategy can produce fast surrogate models that retain essential physics while learning complex crystallization behavior, enabling accurate and efficient predictions of flow-induced crystallization for industrial polymer processing.

Bio:

Beniam is originally from Houston, Texas. He received his Bachelor’s Degree in Chemical Engineering from the University of Texas at Austin in 2025. As an undergraduate, he conducted research on the properties of 2D materials and their applications in energy and memory technologies, combining computational modeling with machine learning approaches. He joined the Rutledge Research Group in Spring 2026 to pursue his interests in scientific machine learning and multiscale modeling of materials.