  Autonomous experimentation enabled by artificial intelligence (AI) offers a
new paradigm for accelerating scientific discovery. Non-equilibrium materials
synthesis is emblematic of complex, resource-intensive experimentation whose
acceleration would be a watershed for materials discovery and development. The
mapping of non-equilibrium synthesis phase diagrams has recently been
accelerated via high throughput experimentation but still limits materials
research because the parameter space is too vast to be exhaustively explored.
We demonstrate accelerated synthesis and exploration of metastable materials
through hierarchical autonomous experimentation governed by the Scientific
Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis
and characterization along with a hierarchy of AI methods that efficiently
reveal the structure of processing phase diagrams. SARA designs lateral
gradient laser spike annealing (lg-LSA) experiments for parallel materials
synthesis and employs optical spectroscopy to rapidly identify phase
transitions. Efficient exploration of the multi-dimensional parameter space is
achieved with nested active learning (AL) cycles built upon advanced machine
learning models that incorporate the underlying physics of the experiments as
well as end-to-end uncertainty quantification. With this, and the coordination
of AL at multiple scales, SARA embodies AI harnessing of complex scientific
tasks. We demonstrate its performance by autonomously mapping synthesis phase
boundaries for the Bi$_2$O$_3$ system, leading to orders-of-magnitude
acceleration in establishment of a synthesis phase diagram that includes
conditions for kinetically stabilizing $\delta$-Bi$_2$O$_3$ at room
temperature, a critical development for electrochemical technologies such as
solid oxide fuel cells.
