Enhanced-sampling algorithms relying on collective variables (CVs) are extensively
employed to study complex (bio) chemical processes that are not amenable to brute-force …

In the last quarter-century, the field of molecular dynamics (MD) has undergone a
remarkable transformation, propelled by substantial enhancements in software, hardware …

Chemical space modelling has great importance in unveiling and visualising latent
information, which is critical in predictive toxicology related to drug discovery process. While …

Determining collective variables (CVs) for conformational transitions is crucial to
understanding their dynamics and targeting them in enhanced sampling simulations. Often …

We present a time-dependent variational method to learn the mechanisms of equilibrium
reactive processes and efficiently evaluate their rates within a transition path ensemble. This …

Over the past few years, different types of data-driven Artificial Intelligence (AI) techniques
have been widely adopted in various domains of science for generating predictive models …

The primary goal of protein science is to understand how proteins function, which requires
understanding the functional dynamics responsible for transitions between different …

Choosing an appropriate collective variable (CV) for any biomolecular process is a
challenging task. Researchers are developing methods to solve this issue using a variety of …

We use local diffusion maps to assess the quality of two types of collective variables (CVs)
for a recently published hydrogen combustion benchmark dataset1 that contains ab initio …

In the industrial production of chloramphenicol, a ternary azeotrope consisting of
isopropanol, benzene and water exists. A new process for the extractive distillation of …