High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches

Abstract

Antisolvent crystallization methods are frequently used to fabricate high-quality perovskite thin films, to produce sizable single crystals, and to synthesize nanoparticles at room temperature.However, a systematic exploration of the effect of specific antisolvents on the intrinsic stability of multicomponent metal halide perovskites has yet to be demonstrated. Here, we develop a highthroughput experimental workflow that incorporates chemical robotic synthesis, automated characterization, and machine learning techniques to explore how the choice of antisolvent affects the intrinsic stability of binary perovskite systems in ambient conditions over time. Different combinations of the endmembers, MAPbI3, MAPbBr3, FAPbI3, FAPbBr3, CsPbI3, and CsPbBr3, are used to synthesize 15 combinatorial libraries, each with 96 unique combinations. In total, roughly 1100 different compositions are synthesized. Each library is fabricated twice using two different antisolvents: toluene and chloroform. Once synthesized, photoluminescence spectroscopy is automatically performed every 5 minutes for approximately 6 hours. Non-negative Matrix Factorization (NMF) is then utilized to map the time-and compositional-dependent optoelectronic properties. Through the utilization of this workflow for each library, we demonstrate that the selection of antisolvent is critical to the stability of metal halide perovskites 2 in ambient conditions. We explore possible dynamical processes, such as halide segregation, responsible for either the stability or eventual degradation as caused by the choice of antisolvent.Overall, this high-throughput study demonstrates the vital role that antisolvents play in the synthesis of high-quality multicomponent metal halide perovskite systems.

“Green” synthesis of highly luminescent lead-free Cs2AgxNa1−xBiyIn1−yCl6 perovskites

“…[22][23][24] The feasibility and efficiency of high-throughput experiments in the field of lead perovskite photovoltaics were recently demonstrated. [25][26][27][28][29][30] However, the majority of the reported syntheses of double LFPs require high energy input by hydrothermal treatment or calcination. Furthermore, a considerable amount of manual intermediate steps is needed which cannot be automated, as well as the usage of hazardous chemicals such as concentrated HCl in the case of chloride LFPs 1,3,6,7,9,14,17,21 or unstable and aggressive substances for bromide and iodide LFPs.…”

“…22–24 The feasibility and efficiency of high-throughput experiments in the field of lead perovskite photovoltaics were recently demonstrated. 25–30…”

Materials Data toward Machine Learning: Advances and Challenges

“…52−55 In addition, embracing automated robots in the materials lab to perform a series of predesigned experiments can also realize HT synthesis, as illustrated in Figure 1c where 1100 metal halide perovskite thin films with different compositions were fabricated through robotics-based systhesis. 56 HT fabrication is generally followed by HT characterization, as shown in Figure 1d, in which various analysis tools are integrated for on-chip characterization. 57 It is worth noting that one alternative method for HT characterization is increasing the efficiency of a single detector to gain more data.…”

“…112 It was also demonstrated that with this procedure the calculated critical temperature of decomposition and decomposed phases agree with experimental observation, using the example of high-entropy alloys. 112 The potential large data volume for multicomponent materials is suitable for the application of ML-aided material design, as demonstrated in various multicomponent systems, 56,113 including the high-entropy cases. 114−117 The publication of raw data and ML code with the main results should be encouraged for the study of multicomponent materials using high-throughput methods followed by machine learning techniques.…”

“…For HT fabrication and characterization, upgrading and integration of available experimental equipment are normally required. Combinatorial synthesis by co-depositing from multiple sources or by multilayer deposition is a popular HT fabrication approach, through which materials with compositional or (micro)­structural diversity can be formed on one substrate. − In addition, embracing automated robots in the materials lab to perform a series of predesigned experiments can also realize HT synthesis, as illustrated in Figure c where 1100 metal halide perovskite thin films with different compositions were fabricated through robotics-based systhesis . HT fabrication is generally followed by HT characterization, as shown in Figure d, in which various analysis tools are integrated for on-chip characterization .…”