A self-driving physical vapor deposition system making sample-specific decisions on the fly
Year: 2025
Type: article
Abstract: We present an autonomous physical vapor deposition system that integrates hardware automation, in-situ optical spectroscopy, and Bayesian machine learning into a complete self-driving laboratory framework making decisions on the fly. Using silver thin films as a model material, our platform efficiently navigates a complex parameter space through active learning. By introducing a thin physical layer denoted as calibration layer, the machine learning models adapt to sample-specific conditions on the fly and reliably predict the deposition conditions to achieve user-specified optical properties. Moreover, from the high-throughput experimental data, the algorithm systematically captures the complex parameter-property relationships that are challenging to deduce by conventional trial-and-error methods. This study demonstrates the potential of self-driving laboratories for both reducing human labor and gaining new understanding of materials, providing a streamlined approach to enable self-driving physical vapor deposition systems.
Source: npj Computational Materials
Authors: Yuanlong Bill Zheng, Connor Blake, Layla Mravac, Fengxue Zhang, Yuxin Chen, Shuolong Yang
Institution: University of Chicago
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
Awards: NNCI: Soft Hybrid Nanotechnology Experimental (SHyNE) Resource, G2233802770, G3010471893, Materials Research Science and Engineering Center, FMSG: Cyber: Self-Learning Robotic Epitaxy for Superconducting Quantum Circuitry, G4657484576, G4958141111, MRI: Development of a Miniaturized Molecular Beam Epitaxy Setup for Direct Printing of Quantum Circuits, G8048112891, G8411689924
OpenAlex ID: W4415930067