A data fusion approach to optimize compositional stability of halide perovskites

Sun, Shijing; Stevanovic, Vladan; Oviedo, Felipe; Ren, Zekun; Liu, Zhe; Yoo, Jason J.; Tiihonen, Armi; Goyal, Anuj; Peters, I. Marius; Brabec, Christoph J.; Encinas, Alex; Zhao, Yicheng; Batali, Clio; Buonassisi, Tonio; Li, Ruipeng; Heumueller, Thomas; Thapa, Janak; Fisher, John; Bawendi, Moungi G.; Hartono, Noor Titan P.

doi:10.1016/j.matt.2021.01.008

Journal Article

FZJ-2021-01419

A data fusion approach to optimize compositional stability of halide perovskites

Sun, S. (Corresponding author) ; Tiihonen, A. ; Oviedo, F. ; Liu, Z. ; Thapa, J. ; Zhao, Y.FZJ* ; Hartono, N. T. P. ; Goyal, A. ; Heumueller, T.FZJ* ; Batali, C. ; Encinas, A. ; Yoo, J. J. ; Li, R. ; Ren, Z. ; Peters, I. M.FZJ* ; Brabec, C. J.FZJ* ; Bawendi, M. G. ; Stevanovic, V. ; Fisher, J. ; Buonassisi, T.

2021
Elsevier [New York, NY]

Matter 4(4), 1305-1322 (2021) [10.1016/j.matt.2021.01.008]

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Please use a persistent id in citations: http://hdl.handle.net/2128/27593 doi:10.1016/j.matt.2021.01.008

Abstract: Despite recent intensive efforts to improve the environmental stability of halide perovskite materials for energy harvesting and conversion, traditional trial-and-error explorations face bottlenecks in the navigation of vast chemical and compositional spaces. We develop a closed-loop optimization framework that seamlessly marries data from first-principle calculations and high-throughput experimentation into a single machine learning algorithm. This framework enables us to achieve rapid optimization of compositional stability for CsxMAyFA1−x−yPbI3 perovskites while taking the human out of the decision-making loop. We envision that this data fusion approach is generalizable to directly tackle challenges in designing multinary materials, and we hope that our successful showcase on perovskites will encourage researchers in other fields to incorporate knowledge of physics into the search algorithms, applying hybrid machine learning models to guide discovery of materials in high-dimensional spaces.

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