5 research outputs found
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Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion.
Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation
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Spatiotemporal Route to Understanding Metal Halide Perovskitoid Crystallization
A spatiotemporal experimental route is reported for the antisolvent vapor diffusion crystal growth of metal halide perovskitoids. A computational analysis combining automated image capture and diffusion modeling enables the determination of the critical concentrations required for nucleation and crystal growth from a single experiment. Five different solvent systems and ten distinct organic ammonium iodide salts were investigated with lead iodide, from which nine previously unreported compounds were discovered. Automated image capture of the mother liquor and antisolvent vials was used to determine changes in solution meniscus positions and detect the nucleation event location. Matching the observations to a numerical solution of Fick's second law diffusion model enables the calculation of reactant, solvent, and antisolvent concentrations at both the time and position of the first stable nucleation and crystal growth. A machine learning model was trained on the resulting data, and it reveals solvent- and amine-specific crystallization tendencies. Solvent systems that interact more weakly with dissolved lead species promote crystallization, while those with stronger interactions can prevent crystallization through increased solubilities. Organic amines that interact more strongly with inorganic components and exhibit greater rigidity are more likely to be incorporated into crystalline products
Using automated serendipity to discover how trace water promotes and inhibits lead halide perovskite crystal formation
We use a data-driven approach to discover the influence of trace amounts of water on perovskite crystal formation. Statistical analysis of 8,470 inverse-temperature crystallization lead iodide perovskite synthesis reactions, performed over 20 months using a robotic system, revealed discrepancies between the empirical crystal formation rate in experiments conducted under different ambient relative humidity conditions. We used the robotic system to conduct 1,296 controlled interventional experiments in which small amounts of water were deliberately introduced to the reactions. Addition of trace amounts of water promotes crystal formation for 4-methoxyphenylammonium lead iodide and iso-propylammonium lead iodide and inhibits crystal formation for dimethylammonium lead iodide and acetamidinium lead iodide. We also performed thin-film syntheses of these four materials and determined the grain size distributions using scanning electron microscopy. Addition of water results in smaller grain sizes for dimethylammonium and larger grain sizes for isopropylammonium, consistent with earlier or delayed nucleation, respectively