Established a comprehensive framework for correlative and causal machine learning (ML)-based discovery and optimization of halide perovskites.
Significance and Impact
The proposed framework bridges the gap between instrumentation and control, leading to efficient automation of synthesis and characterization of metal halide perovskites (MHPs).
Mashid Ahmadi, Maxim Ziatdinov, Yuanyuan Zhou, Eric A. Lass, and Sergei V. Kalinin, "Machine Learning for High-Throughput Experimental Exploration of Metal Halide Perovskites," Joule (2021). DOI: 10.1016/j.joule.2021.10.001