We are developing machine learning-driven autonomous research systems, designed with the goal of accelerating the discovery and optimization of advanced materials. These systems combine machine learning with machine-controlled materials synthesis and characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior physics knowledge into the data analysis, including both physics theory and databases of experimental and computational materials property data.

We currently run 10 diverse autonomous platforms including machine learning control of neutron scattering, pulsed laser deposition, and additive manufacturing. We are particularly interested in using these autonomous systems to verify and identify the synthesis-process-structure-property relationship for quantum solid state materials. 

References:

Liang, et al., 2025. Real-time experiment-theory closed-loop interaction for autonomous materials science. Science Advances, 11(27), p.eadu7426.

Kusne, et al., 2020. On-the-fly closed-loop materials discovery via Bayesian active learning. Nature communications, 11(1), p.5966.

key words

Materials Genome Initiative; Autonomous; Machine learning; Informatics; High-throughput; Data mining; Functional materials; Active Learning

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants