Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by in Situ Spectroscopy and Automation

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD. This study demonstrates autonomous synthesis of thin film materials using the highly versatile pulsed laser deposition technique. Through a combination of in situ and real-time spectroscopy, a high-throughput methodology, automation, and machine learning, a 4D parameter space is efficiently explored 10x faster than traditional workflows to discover two optimal growth windows for the 2D material WSe2. image