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Synthesis of thin films has traditionally relied upon slow, sequentialprocesses carried out with substantial human intervention, frequently utilizinga mix of experience and serendipity to optimize material structure andproperties. With recent advances in autonomous systems which combine synthesis,characterization, and decision making with artificial intelligence (AI), largeparameter spaces can be explored autonomously at rates beyond what is possibleby human experimentalists, greatly accelerating discovery, optimization, andunderstanding in materials synthesis which directly address the grandchallenges in synthesis science. Here, we demonstrate autonomous synthesis of acontemporary 2D material by combining the highly versatile pulsed laserdeposition (PLD) technique with automation and machine learning (ML). Weincorporated 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 a10x increase in throughput over traditional PLD workflows. Gaussian processregression and Bayesian optimization were used with in situ Raman spectroscopyto autonomously discover two distinct growth windows and the process-propertyrelationship after sampling only 0.25% of a large 4D parameter space. Anymaterial that can be grown with PLD could be autonomously synthesized with ourplatform and workflows, enabling accelerated discovery and optimization of avast number of materials.

Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation