  The committor function is a central object of study in understanding
transitions between metastable states in complex systems. However, computing
the committor function for realistic systems at low temperatures is a
challenging task, due to the curse of dimensionality and the scarcity of
transition data. In this paper, we introduce a computational approach that
overcomes these issues and achieves good performance on complex benchmark
problems with rough energy landscapes. The new approach combines deep learning,
data sampling and feature engineering techniques. This establishes an
alternative practical method for studying rare transition events between
metastable states in complex, high dimensional systems.
