pyDSM: Fast Quantitative Rheology Predictions for Entangled Polymers in Python

It has been extensively shown that the discrete slip-link model (DSM) accurately predicts the linear and nonlinear rheology of various entangled polymer systems. The only publicly available implementation of the DSM algorithm is written in the CUDA C++ programming language for GPU computing. In this work we discuss the implementation of the fixed slip-link model and the clustered fixed slip-link model in Python. Our work shows that Python can also utilize GPUs to enable fast quantitative rheological predictions. Our simulation code, named pyDSM, allows an easy-to-read and beginner-friendly approach for users wanting to utilize the efficiency of GPU computing while also enabling an open-source Python package that can easily couple or interact with other simulation or data analysis software. We demonstrate pyDSM's versatility by implementing a recently published algorithm that allows estimation of the statistical uncertainty in the autocorrelations for any time series data, accounting properly for the correlation in the data. Here the method is used to calculate the uncertainty in the relaxation modulus and the chain center-of-mass mean squared displacement. Moreover, the uncertainty quantification in the relaxation modulus allows propagation of error through a multi-mode Maxwell fit to determine the uncertainty in the dynamic modulus. Lastly, we include benchmarks and discuss capabilities and limitations of the publicly-available pyDSM package.