Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal Xray diffraction studies. We present the first automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 1928 metal halide perovskite synthesis reactions were conducted using six organic ammonium cations (methylammonium, ethylammonium, nbutylammonium, formamidinium, guanidinium, and acetamidinium), increasing the number of metal halide perovskite materials accessible by ITC syntheses by three and resulting in the formation of a new phase, [C2H7N2][PbI3]. This comprehensive dataset allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this dataset enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.

Abstract

Organic-inorganic halide perovskites have rapidly grown as favorable materials for photovoltaic applications, but accomplishing long-term stability is still a major research problem. This work demonstrates a new insight on instability and degradation factors in CH3NH3PbI3 perovskite solar cells aging with time in open air. X-ray photoelectron spectroscopy (XPS) has been used to investigate the compositional changes caused by device degradation over the period of 1000 hrs. XPS spectra confirm the migration of metallic ions from the bottom electrode (ITO) as a key factor causing the chemical composition change in the perovskite layer besides the diffusion of oxygen. XPS results are in good agreement with the crystallographic marks. Glow discharge optical emission spectrometry (GD-OES) has also been performed on the samples to correlate the XPS results. Based on the experimental results, fundamental features that account for the instability in the perovskite solar cell is discussed.

In this paper, organic‐based capacitive and resistive humidity sensors, starting from the fundamental working principles, designs, and characterization, are reviewed. The prevalent adoptions of recent technology in the fabrication of the organic humidity sensors are also discussed. This paper is envisioned to serve as a complete reference guide for researchers who are looking forward to developing a more reliable, economical, and effective humidity sensor using organic/polymeric materials.

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Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI3 phase ([C4H10NO][PbI3]) and a 2D (morph)2PbI4 phase ([C4H10NO]2[PbI4]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we propose a reaction scheme that rationalizes the formation of different dimensional MHP derivatives in the morph-Pb-I system. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.

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Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.