Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

Journal Article · Sat Jun 01 00:00:00 EDT 2019 · Joule

DOI:https://doi.org/10.1016/j.joule.2019.05.014· OSTI ID:1633921

Sun, Shijing; Hartono, Noor T. P.; Ren, Zekun D.; Oviedo, Felipe; Buscemi, Antonio M.; Layurova, Mariya; Chen, De Xin; Ogunfunmi, Tofunmi; Thapa, Janak; Ramasamy, Savitha; Settens, Charles; DeCost, Brian L.; Kusne, Aaron G.; Liu, Zhe; Tian, Siyu I. P.; Peters, Ian Marius; Correa-Baena, Juan-Pablo; Buonassisi, Tonio

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 halide perovskite-inspired solution- based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a 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 novel lead-free compositions. The wider synthesis window and faster cycle of learning enables three noteworthy scientific findings: (1) we realize four inorganic layered perovskites, A₃B₂Br₉ (A = Cs, Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report a multi-site lead-free alloy series that was not previously described in literature, Cs₃(Bi_1-xSb_x)₂(I_{1- x}Br_x)₉; and (3) we reveal the effect on bandgap (reduction to <2 eV) and structure upon simultaneous alloying on the B-site and X-site of Cs₃Bi₂I₉ with Sb and Br. This study demonstrates that combining an accelerated experimental cycle of learning and machine-learning based diagnosis represents an important step toward realizing fully-automated laboratories for materials discovery and development.

• Research Organization: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; National Science Foundation (NSF); Singapore National Research Foundation (NRF); Agency for Science, Technology and Research
• Grant/Contract Number: EE0007535; CBET-1605547; A1898b0043
• OSTI ID: 1633921
• Alternate ID(s): OSTI ID: 1562968; OSTI ID: 1865940
• Journal Information: Joule, Journal Name: Joule Vol. 3 Journal Issue: 6; ISSN 2542-4351
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
14 SOLAR ENERGY