Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …

Coarse‐grained (CG) modeling is an invaluable tool for the study of polymers and other soft
matter systems due to the span of spatiotemporal scales that typify their physics and …

Preserving the correct dynamics at the coarse-grained (CG) level is a pressing problem in
the development of systematic CG models in soft matter simulation. Starting from the seminal …

We consider the Brownian motion of a particle and present a tutorial review over the last 111
years since Einstein's paper in 1905. We describe Einstein's model, Langevin's model and …

Polymer materials are multiscale systems by definition. Already the description of a single
macromolecule involves a multitude of scales, and cooperative processes in polymer …

There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-
eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some recent …

The application of deep learning toward discovery of data-driven models requires careful
application of inductive biases to obtain a description of physics which is both accurate and …

We introduce a method to accurately and efficiently estimate the effective dynamics of
collective variables in molecular simulations. Such reduced dynamics play an essential role …

We present a data-driven approach to determine the memory kernel and random noise in
generalized Langevin equations. To facilitate practical implementations, we parameterize …

In recent years, it has become increasingly popular to construct coarse-grained models with
non-Markovian dynamics to account for an incomplete separation of time scales. One …