Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys

Wang, Tonghui; Li, Ruipeng; Ardekani, Hossein; Serrano-Luján, Lucía; Wang, Jiantao; Ramezani, Mahdi; Wilmington, Ryan; Chauhan, Mihirsinh; Epps, Robert W.; Darabi, Kasra; Guo, Boyu; Sun, Dali; Abolhasani, Milad; Gündoğdu, Kenan; Amassian, Aram

doi:10.1016/j.matt.2023.06.040

Cited by 9 publications

(5 citation statements)

References 71 publications

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“…Pioneering efforts in material acceleration platforms have oen generated proxy samples in model architectures that are then used to drive experiments in standard architectures. [25][26][27] PASCAL can fabricate standard thin lms and sub-cells for direct investigation e.g. of perovskite materials and their interfaces with transport layers for direct integration into solar cells.…”

Section: Pascal Overviewmentioning

confidence: 99%

“…19 Robotic automation of halide perovskite experiments is growing in prevalence, improving experimental throughput and process precision. 12,[20][21][22][23][24][25][26][27][28][29] Such automation has been commonplace in drug discovery, facilitated by liquid handling hardware to execute solution-based experiments. Automation of thin lm experiments is somewhat more difficult because solid samples require more advanced hardware to manipulate than liquids.…”

Section: Introductionmentioning

confidence: 99%

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PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys

Cakan,

Kumar,

Oberholtz

et al. 2024

Digital Discovery

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Section: Pascal Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys

Cakan,

Kumar,

Oberholtz

et al. 2024

Digital Discovery

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“…Previous studies suggested that photo-induced halide segregation was caused by factors such as charge carrier gradients [34,36,37] , polaron-induced strain gradients under illumination [36,[38][39][40] , or thermodynamic origins [41][42][43] . These theories indicate that the bandgap differences between mixed-halide perovskites are a crucial factor contributing to halide segregation under illumination, which means that the bandgaps significantly influence the segregation behavior [33,34,44,45] .…”

Section: Introductionmentioning

confidence: 99%

Data-driven strategy for bandgap database construction of perovskites and the potential segregation study

Wu,

Zhang,

Wang

et al. 2024

J Mater Inf

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Light-induced segregation limits the practical application of mixed halide perovskites in solar cells. Herein, halide segregation is evaluated by a data-driven approach with constructing a bandgap database of 53,361 mixed ABX3 [where A = Cs, formamidinium (FA) or methylammonium (MA); B = Pb or Sn; X = Br, Cl, or I] perovskites. A transfer learning strategy was employed to fine-tune the parameters of a Graph Neural Network model using experimental and density functional theory (DFT)-calculated bandgaps. This approach accelerated the construction of a unique database, distinguishing it from others primarily focused on ABX3 perovskite element substitution. The database is characterized by continuously varying compositions and accurate bandgaps. It was utilized to calculate the free energy of 20,688 mixed iodine-bromine perovskites and generate corresponding phase diagrams for predicting their light-induced segregation behavior. It is found that the bandgap increases with decreasing ionic radii at the A-site and X-site. This composition-dependent bandgap difference drives halide segregation. Moreover, using a higher Cs content at the A-site, rather than MA, reduces this bandgap difference, enhancing photostability. The proposed data-driven strategy can facilitate the targeted design of novel perovskites with mixed compositions and the investigation of halide perovskite segregation.

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“…demonstrate an adaptive strategy where Bayesian optimization locates the range of parameters of interest for the optimisation process before conducting a local exploration of parameter values to identify parameters that produce high-efficiency devices. The RoboMapper testing process [31] has the potential to generate data from measurements, which is crucial for training ML models. Bayesian Optimisation adapted to allow for Symmetry Relaxation has been shown to obtain equilibrium crystal structures for accurate ML property predictions without the need for DFT calculations [32].…”

Section: Introductionmentioning

confidence: 99%

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

McCallum,

Nicholls,

Jensen

et al. 2023

J. Phys. Energy

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To overcome the challenges associated with poor temporal stability of perovskite solar cells, methods are required that allow for fast iteration of fabrication and characterisation, such that optimal device performance and stability may be actively pursued. Currently, establishing the causes of underperformance is both complex and time-consuming, and optimisation of device fabrication is thus inherently slow. Here, we present a means of computational device characterisation of mobile halide ion parameters from room temperature current–voltage ( J − V ) measurements only, requiring ∼2 h of computation on basic computing resources. With our approach, the physical parameters of the device may be reverse-modelled from experimental J − V measurements. In a drift-diffusion (DD) model, the set of coupled DD partial differential equations cannot be inverted explicitly, so a method for inverting the DD simulation is required. We show how Bayesian Parameter Estimation coupled with a DD perovskite solar cell model can determine the extent to which device parameters affect performance measured by J − V characteristics. Our method is demonstrated by investigating the extent to which device performance is influenced by mobile halide ions for a specific fabricated device. The ion vacancy density N 0 and diffusion coefficient DI were found to be precisely characterised for both simulated and fabricated devices. This result opens up the possibility of pinpointing origins of degradation by finding which parameters most influence device J − V curves as the cell degrades.

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Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys

Cited by 9 publications

References 71 publications

PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys

PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys

Data-driven strategy for bandgap database construction of perovskites and the potential segregation study

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

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