  One of the most exciting applications of artificial intelligence (AI) is
automated scientific discovery based on previously amassed data, coupled with
restrictions provided by known physical principles, including symmetries and
conservation laws. Such automated hypothesis creation and verification can
assist scientists in studying complex phenomena, where traditional physical
intuition may fail. Here we develop a platform based on a generalized Onsager
principle to learn macroscopic dynamical descriptions of arbitrary stochastic
dissipative systems directly from observations of their microscopic
trajectories. Our method simultaneously constructs reduced thermodynamic
coordinates and interprets the dynamics on these coordinates. We demonstrate
its effectiveness by studying theoretically and validating experimentally the
stretching of long polymer chains in an externally applied field. Specifically,
we learn three interpretable thermodynamic coordinates and build a dynamical
landscape of polymer stretching, including the identification of stable and
transition states and the control of the stretching rate. Our general
methodology can be used to address a wide range of scientific and technological
applications.
