  Synthesis of thin films has traditionally relied upon slow, sequential
processes carried out with substantial human intervention, frequently utilizing
a mix of experience and serendipity to optimize material structure and
properties. With recent advances in autonomous systems which combine synthesis,
characterization, and decision making with artificial intelligence (AI), large
parameter spaces can be explored autonomously at rates beyond what is possible
by human experimentalists, greatly accelerating discovery, optimization, and
understanding in materials synthesis which directly address the grand
challenges in synthesis science. Here, we demonstrate autonomous synthesis of a
contemporary 2D material by combining the highly versatile pulsed laser
deposition (PLD) technique with automation and machine learning (ML). We
incorporated in situ and real-time spectroscopy, a high-throughput methodology,
and cloud connectivity to enable autonomous synthesis workflows with PLD.
Ultrathin WSe2 films were grown using co-ablation of two targets and showed a
10x increase in throughput over traditional PLD workflows. Gaussian process
regression and Bayesian optimization were used with in situ Raman spectroscopy
to autonomously discover two distinct growth windows and the process-property
relationship after sampling only 0.25% of a large 4D parameter space. Any
material that can be grown with PLD could be autonomously synthesized with our
platform and workflows, enabling accelerated discovery and optimization of a
vast number of materials.
