  The ability to make sense of the massive amounts of high-dimensional data
generated from molecular dynamics (MD) simulations is heavily dependent on the
knowledge of a low dimensional manifold (parameterized by a reaction coordinate
or RC) that typically distinguishes between relevant metastable states and
which captures the relevant slow dynamics of interest. Methods based on machine
learning and artificial intelligence have been proposed over the years to deal
with learning such low-dimensional manifolds, but they are often criticized for
a disconnect from more traditional and physically interpretable approaches. To
deal with such concerns, in this work, we propose a deep learning based State
Predictive Information Bottleneck (SPIB) approach to learn the RC from high
dimensional molecular simulation trajectories. We demonstrate analytically and
numerically how the RC learnt in this approach is deeply connected to the
committor in chemical physics, and can be used to accurately identify
transition states. A crucial hyperparameter in this approach is the time-delay,
or how far into the future the algorithm should make predictions about. Through
careful comparisons for benchmark systems, we demonstrate that this
hyperparameter choice gives useful control over how coarse-grained we want the
metastable state classification of the system to be. We thus believe that this
work represents a step forward in systematic application of deep learning based
ideas to molecular simulations in a way that bridges the gap between artificial
intelligence and traditional chemical physics.
