A Fully Robotic Platform for Optimizing the High-Dimensional Processing Parameter Space of Perovskite Thin-Films

Zhang, Jiyun; Liu, Bowen; Liu, Ziyi; Wu, Jianchang; Arnold, Simon; Shi, Hongyang; Osterrieder, Tobias; Hauch, Jens; Wu, Zhenni; Luo, Junsheng; Wagner, Jerrit; Berger, Christian; Stubhan, Tobias; Schmitt, Frederik; Zhang, Kaicheng; Sytnyk, Mykhailo; Heumueller, Thomas; Sutter‐Fella, Carolin M.; Peters, Ian Marius; Zhao, Yicheng; Brabec, Christoph J.

doi:10.2139/ssrn.4309089

Cited by 5 publications

(6 citation statements)

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“…The step-by-step optimization process identified an optimal condition, which resulted in a champion perovskite device with a PCE of 21%. 3 The exceptional manufacturing process control and data reproducibility offered by this automated DAP were exemplified through repeating device fabrication under optimal conditions. Recognizing the limitations of this approach, such as limited parameter ranges and possible local optimizations, a Bayesian optimization algorithm was then introduced to direct the DAP for global optimization.…”

Section: Perovskite Photovoltaics Materialsmentioning

confidence: 99%

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies

Zhang,

Hauch,

Brabec

2024

Acc. Chem. Res.

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Metrics & MoreArticle Recommendations CONSPECTUS: In the ever-increasing renewable-energy demand scenario, developing new photovoltaic technologies is important, even in the presence of established terawatt-scale silicon technology. Emerging photovoltaic technologies play a crucial role in diversifying material flows while expanding the photovoltaic product portfolio, thus enhancing security and competitiveness within the solar industry. They also serve as a valuable backup for silicon photovoltaic, providing resilience to the overall energy infrastructure. However, the development of functional solar materials poses intricate multiobjective optimization challenges in a large multidimensional composition and parameter space, in some cases with millions of potential candidates to be explored. Solving it necessitates reproducible, user-independent laboratory work and intelligent preselection of innovative experimental methods. Materials acceleration platforms (MAPs) seamlessly integrate robotic materials synthesis and characterization with AI-driven data analysis and experimental design, positioning them as enabling technologies for the discovery and exploration of new materials. They are proposed to revolutionize materials development away from the Edisonian trial-and-error approaches to ultrashort cycles of experiments with exceptional precision, generating a reliable and highly qualitative data situation that allows training machine learning algorithms with predictive power. MAPs are designed to assist the researcher in multidimensional aspects of materials discovery, such as material synthesis, precursor preparation, sample processing and characterization, and data analysis, and are drawing escalating attention in the field of energy materials. Device acceleration platforms (DAPs), however, are designed to optimize functional films and layer stacks. Unlike MAPs, which focus on material discovery, a central aspect of DAPs is the identification and refinement of ideal processing conditions for a predetermined set of materials. Such platforms prove especially invaluable when dealing with "disordered semiconductors," which depend heavily on the processing parameters that ultimately define the functional properties and functionality of thin film layers. By facilitating the fine-tuning of processing conditions, DAPs contribute significantly to the advancement and optimization of disordered semiconductor devices, such as emerging photovoltaics.In this Account, we review the recent advancements made by our group in automated and autonomous laboratories for advanced material discovery and device optimization with a strong focus on emerging photovoltaics, such as solution-processing perovskite solar cells and organic photovoltaics. We first introduce two MAPs and two DAPs developed in-house: a microwave-assisted highthroughput synthesis platform for the discovery of organic interface materials, a multipurpose robot-based pipetting platform for the synthesis of new semiconductors and the characterization of thin film semi...

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Section: Perovskite Photovoltaics Materialsmentioning

confidence: 99%

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies

Zhang,

Hauch,

Brabec

2024

Acc. Chem. Res.

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“…[ 70,71 ] Utilizing large datasets such as these provides incalculable insights into systems development, especially when combined with ML‐based models. [ 72,73 ]…”

Section: Investigation and Development Of Ppv In Silicomentioning

confidence: 99%

Wide‐Gap Perovskites for Indoor Photovoltaics

Burwell,

Zeiske,

Caprioglio

et al. 2024

Solar RRL

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Organic–inorganic halide perovskite semiconductors have revolutionized next‐generation photovoltaics (PV) due to several characteristics such as solution‐processability, gap tunability, and excellent charge generation and transport properties. This has made them very adaptable for various applications in light harvesting and photodetection. One such rapidly growing application is indoor photovoltaics (IPV) which have the potential to power standalone Internet of Things devices. IPV requires wider optimal bandgaps than solar cells (1.8 vs 1.3 eV) due to the differences between the spectra of artificial lights versus solar radiation. For IPV applications, the active layer wide‐gap perovskite must be developed systemically considering all other components of the device, such as interlayers, electrodes, and scaling. This perspective provides an overview of the potential and challenges facing perovskite‐based IPV from a theoretical, material, and experimental perspective. Furthermore, accurate characterization of perovskite IPVs under simulated indoor conditions is discussed and candidate perovskite PV (PPV) systems are presented to provide insight into IPV development. These include IPV‐optimized formamidinium cesium‐based perovskite, wide‐gap p‐i‐n devices, and 2D perovskite devices, tested under spectrophotometrically calibrated LED illumination at various indoor‐relevant illuminances and benchmarked against thermodynamic predictions. Finally, strategies required to create stable, optimized PPV devices for indoor applications are discussed.

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“…[ 26,27 ] To date, the ML models applied in the context of PSCs have mainly focused on the prediction of perovskite properties, such as the band gap, stability, ionic conductivities, solar cell performance, and other transport properties. [ 4,28–37 ] Only a few models have been used to predict the hole mobility of the solar cells. [ 38,39 ] All of these ML models focused on perovskite, and the reduced number of HTMs limited the scope of the models to link the role of the HTM with the performance of the cell.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells

Rashid,

Lee,

Kim

et al. 2024

Advcd Theory and Sims

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Traditional computational approaches such as Monte Carlo simulation, molecular dynamics, and density functional theory (DFT) have contributed to understanding the role of hole mobility in the development of suitable hole transporting materials (HTMs) for perovskite solar cell efficiency. However, these methods often involve significant computational expenses, thereby limiting the number of feasible studies and hindering the extraction of valuable structure−property guidelines for a rational design of novel HTMs. To address these challenges, this study proposes an ultrafast predictive model that balances prediction accuracy and time efficiency, a critical aspect for predicting the hole mobility of HTMs for perovskite optimization. The model, which leverages the Random Forest learning algorithm, enables comprehensive and rapid analysis of photovoltaic features taken from previous experiments/literature and processed with the RDKit Python library. Notably, recognizing the challenges associated with high correlation in the dataset, a method to improve the calculation of feature importance in random forests is applied. The results highlight bertz_ct, chi1, and logP as key predictive features of HTM performance. However, the model underscores the need to uncover additional predictive features based on structure–property relationships to predict the optimized hole mobility. This approach significantly accelerates the discovery process, outperforming prevalent statistical methods in the prediction of hole mobility and HTM performance. This research signifies a pivotal step toward cost‐effective, accelerated stability and efficiency of HTMs, with implications for the advancement of optoelectronic devices.

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A Fully Robotic Platform for Optimizing the High-Dimensional Processing Parameter Space of Perovskite Thin-Films

Cited by 5 publications

References 0 publications

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies

Wide‐Gap Perovskites for Indoor Photovoltaics

Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells

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