Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Explore a data-driven machine learning algorithm for learning collective variables with a multitask neural network in this 18-minute Lennard-Jones Centre discussion group seminar by Dr Lixin Sun from Microsoft Research Cambridge. Discover new methods for labeling atomic configurations and approximating committor functions, and learn how the resulting ML-learned collective variable serves as an effective low-dimensional representation for capturing reaction progress and guiding umbrella sampling to obtain accurate free energy landscapes. Understand how this approach enables automated dimensionality reduction for energy-controlled reactions in complex systems, offering a unified and data-efficient framework that can be trained with limited data and outperforms single-task learning approaches, including autoencoders. Delve into topics such as enhanced sampling, the importance of good collective variables for reaction rate calculations, and the connection between committor functions and cross-entropy loss. Examine practical applications through examples like the SD extended Brown-Müller model and alanine dipeptide.

Intro
Why enhanced sampling and collective variables?
Good collective variables (CVS) are critical for reaction rate calculations
Machine learned collective variables
Connecting committor functions to cross entropy loss
How does it work: SD extended Brown-Müller example
Another simple example: Alanine dipeptide
Summary