A data fusion approach to optimize compositional stability of halide perovskites - Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe; Liu, Zhe; Thapa, Janak; Zhao, Yicheng; Hartono, Noor Titan P.; Goyal, Anuj; Heumüller, Thomas; Batali, Clio; Encinas, Alex; Yoo, Jason J.; et al. (Matter, 2021)
Search for resource-efficient materials in vast compositional spaces is an outstanding challenge in creating environmentally stable perovskite semiconductors. We demonstrate a physics-constrained sequential learning framework to subsequently identify the most stable alloyed organic-inorganic perovskites. We fuse data from high-throughput degradation tests and first-principle calculations of phase thermodynamics into an end-to-end Bayesian optimization algorithm using probabilistic constraints. By sampling just 1.8% of the discretized CsxMAyFA1−x−yPbI3 (MA, methylammonium; FA, formamidinium) compositional space, perovskites centered at Cs0.17MA0.03FA0.80PbI3 show minimal optical change under increased temperature, moisture, and illumination with >17-fold stability improvement over MAPbI3. The thin films have 3-fold improved stability compared with state-of-the-art multi-halide Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3, translating into enhanced solar cell stability without compromising conversion efficiency. Synchrotron-based X-ray scattering validates the suppression of chemical decomposition and minority phase formation achieved using fewer elements and a maximum of 8% MA. We anticipate that this data fusion approach can be extended to guide materials discovery for a wide range of multinary systems. 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. Data fusion combines first-principle calculations and high-throughput experimentation into an end-to-end closed-loop optimization framework, allowing an accelerated search of alloyed halide perovskites in a combinatorial space without human intervention.
CRIS-Team Scopus Importer:2021-03-05
Keywords: Bayesian optimization; data fusion; density-functional theory; energy materials; high-throughput experimentation; machine learning; MAP4: Demonstrate; perovskite stability