  Molecular self-organization driven by concerted many-body interactions
produces the ordered structures that define both inanimate and living matter.
Understanding the physical mechanisms that govern the formation of molecular
complexes and crystals is key to controlling the assembly of nanomachines and
new materials. We present an artificial intelligence (AI) agent that uses deep
reinforcement learning and transition path theory to discover the mechanism of
molecular self-organization phenomena from computer simulations. The agent
adaptively learns how to sample complex molecular events and, on the fly,
constructs quantitative mechanistic models. By using the mechanistic
understanding for AI-driven sampling, the agent closes the learning cycle and
overcomes time-scale gaps of many orders of magnitude. Symbolic regression
condenses the mechanism into a human-interpretable form. Applied to ion
association in solution, gas-hydrate crystal formation, and membrane-protein
assembly, the AI agent identifies the many-body solvent motions governing the
assembly process, discovers the variables of classical nucleation theory, and
reveals competing assembly pathways. The mechanistic descriptions produced by
the agent are predictive and transferable to close thermodynamic states and
similar systems. Autonomous AI sampling has the power to discover assembly and
reaction mechanisms from materials science to biology.
