Highlights•Machine learning and artificial intelligence approaches have been leveraged for
MD.•One machine learning contribution is in removing noise to make MD data human …

The biomolecular modeling field has flourished since its early days in the 1970s due to the
rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase …

In recent years, deep learning has proven to be a viable methodology for surrogate
modeling and uncertainty quantification for a vast number of physical systems. However, in …

Identifying a reduced set of collective variables is critical for understanding atomistic
simulations and accelerating them through enhanced sampling techniques. Recently …

Designing an appropriate set of collective variables is crucial to the success of several
enhanced sampling methods. Here we focus on how to obtain such variables from …

Free energy biasing methods have proven to be powerful tools to accelerate the simulation
of important conformational changes of molecules by modifying the sampling measure …

Fast and stable fluid simulations are an essential prerequisite for applications ranging from
computer-generated imagery to computer-aided design in research and development …

Collective variable-based enhanced sampling methods have been widely used to study
thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced …

Sampling complex free-energy surfaces is one of the main challenges of modern atomistic
simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a …

Abstract Partial Differential Equations (PDEs) are notoriously difficult to solve. In general,
closed form solutions are not available and numerical approximation schemes are …