Poster
in
Workshop: AI4Mat-2024: NeurIPS 2024 Workshop on AI for Accelerated Materials Design
A Physics Enforced Neural Network to Predict Polymer Melt Viscosity
Ayush Jain · Rishi Gurnani · Arunkumar Rajan · Hang Jerry Qi · Rampi Ramprasad
Keywords: [ Additive Manufacturing ] [ Physics Enforced Machine Learning ] [ Polymers ]
Abstract:
Achieving superior polymeric components through additive manufacturing (AM) relies on precise control of rheology. One key rheological property particularly relevant to AM is melt viscosity ( ). Melt viscosity is influenced by polymer chemistry, molecular weight ( ), polydispersity, induced shear rate ( ), and processing temperature ( ). The relationship of with , , and may be captured by parameterized equations. Several physical experiments are required to fit the parameters, so predicting of a new polymer material in unexplored physical domains is a laborious process. Here, we develop a Physics-Enforced Neural Network (PENN) model that predicts the empirical parameters and encodes the aforementioned equations to calculate as a function of polymer chemistry, , polydispersity, , and . We benchmark our PENN against physics-unaware Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) models. Finally, we demonstrate that the PENN offers superior values of when extrapolating to unseen values of , , and for sparsely seen polymers.
Chat is not available.