Collective Variable

Collective variables (CVs) are low-dimensional representations of high-dimensional molecular systems, crucial for efficiently simulating rare events like protein folding or chemical reactions. Current research focuses on data-driven methods, employing machine learning techniques like spectral mapping, Gaussian mixture models, and neural networks, to identify optimal CVs from simulation data, often incorporating enhanced sampling strategies. These advancements enable more accurate and efficient modeling of complex systems, improving the understanding of fundamental processes in chemistry, biology, and materials science. The development of robust and interpretable CVs is key to unlocking the potential of large-scale molecular simulations.