Sun S, Hartono NTP, Ren ZD, Oviedo F, Buscemi AM, Layurova M, Chen DX, Ogunfunmi T, Thapa J, Ramasamy S, Settens C, Decost BL, Kusne AG, Liu Z, Tian SIP, Peters IM, Correa-Baena JP, Buonassisi T (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 3
Pages Range: 1437-1451
Journal Issue: 6
DOI: 10.1016/j.joule.2019.05.014
Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are of interest for energy-harvesting applications. We utilize a fully connected deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures, more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to lead-free compositions. The wider synthesis window and faster cycle of learning enables the realization of a multi-site lead-free alloy series, Cs
APA:
Sun, S., Hartono, N.T.P., Ren, Z.D., Oviedo, F., Buscemi, A.M., Layurova, M.,... Buonassisi, T. (2019). Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis. Joule, 3(6), 1437-1451. https://doi.org/10.1016/j.joule.2019.05.014
MLA:
Sun, Shijing, et al. "Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis." Joule 3.6 (2019): 1437-1451.
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