# A Physics-Enforced Neural Network to Predict Polymer Melt Viscosity

arXiv: https://arxiv.org/abs/2409.05240 · HTML: https://mlsanity.com/paper/2409.05240/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning](https://mlsanity.com/paper/2512.13186/) (2025)
- [Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programming](https://mlsanity.com/paper/2605.29329/) (2026)
- [A Synthetically-accessible Universe of Chemically Recyclable Polymers](https://mlsanity.com/paper/2608.00149/) (2026)

## Abstract (arXiv)

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 (η). Melt viscosity is influenced by polymer chemistry, molecular weight (Mw), polydispersity, induced shear rate (dotγ), and processing temperature (T). The relationship of η with Mw, dotγ, and T may be captured by parameterized equations. Several physical experiments are required to fit the parameters, so predicting η 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 parametrized equations to calculate η as a function of polymer chemistry, Mw, polydispersity, dotγ, and T. 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 η when extrapolating to unseen values of Mw, dotγ, and T for sparsely seen polymers.