Sampling rare events is key to various fields of science, but current methods are inefficient. Asghar and colleagues propose a rare event sampler based on normalizing flow neural networks that requires no prior data or collective variables, works at and out of equilibrium and keeps efficiency constant as events become rarer.
- Solomon Asghar
- Qing-Xiang Pei
- Ran Ni
