Metal halide perovskites have attracted immense interest as a promising material for a variety of optoelectronic and sensing applications. However, issues regarding long-term stability have emerged as the key bottleneck for commercialization. Here, we develop an automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions. We apply it to four model perovskite systems: MA x FA y Cs 1−x−y PbBr 3 , MA x FA y Cs 1−x−y PbI 3 , Cs x FA y MA 1−x−y Pb(Br x+y I 1−x−y ) 3 , and Cs x MA y FA 1−x−y Pb(I x+y Br 1−x−y ) 3 . Non-negative matrix factorization and Gaussian process regression are used to interpolate the photoluminescent behavior of the phase diagram. This interpolative regression analysis helps to distinguish mixtures that form solid solutions from those that segregate into multiple materials, pointing out the most stable regions of the phase diagram. We find stability dependence on composition to be nonuniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other solution-processable materials.

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the

<p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA_xFA_yCs_1-x-yPbBr₃, MA_xFA_yCs_1-x-yPbI₃, (Cs_xFA_yMA_1-x-yPb(Br_x+yI_1-x-y)₃) and (Cs_xMA_yFA_1-x-yPb(I_x+yBr_1-x-y)₃). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p>

<p></p><p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for a variety of optoelectronic and sensing applications. However, issues regarding long-term stability have emerged as the key bottleneck for applications and still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: <a></a><a>MA<i>_x</i>FA<i>_y</i>Cs_{1-<i>x</i>-<i>y</i>}PbBr₃, MA<i>_x</i>FA<i>_y</i>Cs_{1-<i>x</i>-<i>y</i>}PbI₃, Cs<i>_x</i>FA<i>_y</i>MA_{1-<i>x</i>-<i>y</i>}Pb(Br<i>_x</i>_+<i>y</i>I_{1-<i>x</i>-<i>y</i>})₃ and Cs<i>_x</i>MA<i>_y</i>FA_{1-<i>x</i>-<i>y</i>}Pb(I<i>_x</i>_+<i>y</i>Br_{1-<i>x</i>-<i>y</i>})₃</a>. We have both established a new workflow and found out the main tendencies in the mixed cation and anion systems, which led to the discovery of non-trivial composition regions with high stability. The Non-negative Matrix Factorization and Gaussian Process regression are used <i>to</i> <i>interpolate the photoluminescent behavior of vast compositional space</i> and <i>to study the overall behavior of the phase diagram</i>. This interpolative regression analysis helps to distinguish mixtures that form solid solutions from those that segregate into multiple materials, pointing out the most stable regions of the phase diagram. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. <a>This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. </a>Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p><p></p>

<p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA_xFA_yCs_1-x-yPbBr₃, MA_xFA_yCs_1-x-yPbI₃, (Cs_xFA_yMA_1-x-yPb(Br_x+yI_1-x-y)₃) and (Cs_xMA_yFA_1-x-yPb(I_x+yBr_1-x-y)₃). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p>