  Machine learning (ML) has emerged as a pervasive tool in science,
engineering, and beyond. Its success has also led to several synergies with
molecular dynamics (MD) simulations, which we use to identify and characterize
the major metastable states of molecular systems. Typically, we aim to
determine the relative stabilities of these states and how rapidly they
interchange. This information allows mechanistic descriptions of molecular
mechanisms, enables a quantitative comparison with experiments, and facilitates
their rational design. ML impacts all aspects of MD simulations -- from
analyzing the data and accelerating sampling to defining more efficient or more
accurate simulation models.
