Automated design of collective variables using supervised machine learning

Understand

Selection of appropriate collective variables for enhancing sampling of molecular simulations remains an unsolved problem in computational biophysics.

• In particular, picking initial collective variables (CVs) is particularly challenging in higher dimensions.
• Which atomic coordinates or transforms there of from a list of thousands should one pick for enhanced sampling runs? How does a modeler even begin to pick starting coordinates for investigation? This remains true even in the case of simple two state systems and only increases in difficulty for multi-state systems.
• In this work, we solve the initial CV problem using a data-driven approach inspired by the filed of supervised machine learning.