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Achieving high-performance perovskite photovoltaics, especially in ambient air relies heavily on optimizing process parameters. However, traditional manual methods often struggle to effectively control the key variables. This inherent challenge requires a paradigm shift toward automated platforms capable of precise and reproducible experiments. Herein, we use a fully automated device acceleration platform (DAP) to optimize the process parameters for preparing full perovskite devices using a two-step method in ambient air. Eight process parameters that have the potential to significantly influence device performance are systematically optimized. Specifically, we delve into the impact of the dispense speed of organic ammonium halide, a parameter that is difficult to control manually, on both perovskite film and device performance. Through the targeted design of experiments, we reveal that the dispense speed significantly affects device performance primarily by adjusting the residual PbI2 content in the films. We find that moderate dispense speeds, e.g., 50 {\mu}l/s, contribute to top-performance devices. Conversely, too fast or too slow speeds result in devices with relatively poorer performance and lower reproducibility. The optimized parameter set enables us to establish a Standard Operation Procedure (SOP) for additive-free perovskite processing under ambient conditions, which yield devices with efficiencies surpassing 23%, satisfactory reproducibility, and state-of-the-art photo-thermal stability. This research underscores the importance of understanding the causality of process parameters in enhancing perovskite photovoltaic performance. Furthermore, our study highlights the pivotal role of automated platforms in discovering innovative workflows and accelerating the development of high-performing perovskite photovoltaic technologies.

Optimizing solution-processed organic solar cells is a complex task due to the vast parameter space in organic photovoltaics (OPV). Classical Edisonian or one-variable-at-a-time (OVAT) optimization approaches are laborious, time-consuming, and may not find the optimal parameter set in multidimensional design spaces. To tackle this problem, we demonstrate here for the first time artificial intelligence (AI) guided closed-loop autonomous optimization for fully functional organic solar cells. We empower our LineOne, an automated materials and device acceleration platform with a Bayesian Optimizer (BO) to enable autonomous operation for solving complex optimization problems without human interference. The system is able to fabricate and characterize complete OPV devices and navigate efficiently through the design space spanned by composition and processing parameters. In addition, a Gaussian Progress Regression (GPR) based early prediction model is employed to predict the efficiency of the cells from cheap proxy measurements, in our case, thin film absorption spectra, which are analyzed using a spectral model based on physical properties to generate microstructure features as input for the GPR. We demonstrate our generic and complete autonomous approach by optimizing composition and processing conditions of a ternary OPV system (PM6:Y12:PC70BM) in a four-dimensional parameter space. We identify the best parameter set for our system and obtain a precise objective function over the whole parameter space with a minimal number of samples. We demonstrate autonomous optimization of a complex opto-electronic device within 40 samples only, whereas an Edisonian approach would have required about 1000 samples. This raises an important discussion on the necessity of autonomous platforms to accelerate Material science., Comment: 18 pages, 5 figures

The density of states (DOS) is fundamentally important for understanding physical processes in organic disordered semiconductors, yet hard to determine experimentally. We evaluated the DOS by considering recombination via tail states and using the temperature and open-circuit voltage ($V_\mathrm{oc}$) dependence of the ideality factor in organic solar cells. By performing Suns-$V_\mathrm{oc}$ measurements, we find that gaussian and exponential distributions describe the DOS only at a given quasi-Fermi level splitting. The DOS width increases linearly with the DOS depth, revealing the power-law DOS in these materials., Comment: 5 pages, 3 figures

Stability is one of the most important challenges facing organic solar cells (OSC) on their path to commercialization. In the high-performance material system PM6:Y6 studied here, investigate degradation mechanisms of inverted photovoltaic devices. We have identified two distinct degradation pathways: one requires presence of both illumination and oxygen and features a short-circuit current reduction, the other one is induced thermally and marked by severe losses of open-circuit voltage and fill factor. We focus our investigation on the thermally accelerated degradation. Our findings show that bulk material properties and interfaces remain remarkably stable, however, aging-induced defect state formation in the active layer remains the primary cause of thermal degradation. The increased trap density leads to higher non-radiative recombination, which limits open-circuit voltage and lowers charge carrier mobility in the photoactive layer. Furthermore, we find the trap-induced transport resistance to be the major reason for the drop in fill factor. Our results suggest that device lifetimes could be significantly increased by marginally suppressing trap formation, leading to a bright future for OSC.

Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost. So far, solar cell R&D has mostly focused on achieving record efficiencies, a process that often results in small batches, large variance, and limited understanding of the physical causes of underperformance. This approach is intensive in time and resources, and ignores many relevant factors for industrial production, particularly the need for high reproducibility and high manufacturing yield, and the accompanying need of physical insights. The record-efficiency paradigm is effective in early-stage R&D, but becomes unsuitable for industrial translation, requiring a repetition of the optimization procedure in the industrial setting. This mismatch between optimization objectives, combined with the complexity of physical root-cause analysis, contributes to decade-long timelines to transfer new technologies into the market. Based on recent machine learning and technoeconomic advances, our perspective articulates a data-driven optimization framework to bridge R&D and manufacturing optimization approaches. We extend the maximum-efficiency optimization paradigm by considering two additional dimensions: a technoeconomic figure of merit and scalable physical inference. Our framework naturally aligns different stages of technology development with shared optimization objectives, and accelerates the optimization process by providing physical insights.

Potentiostatic impedance spectroscopy (IS) is a well stablished characterization technique for elucidating the electric resistivity and capacitive features of materials and devices. In the case of solar cells, by applying a small voltage perturbation the current signal is recorded and the recombination processes and defect distributions are among the typical outcomes in IS studies. In this work a photo-impedance approach, named light intensity modulated impedance spectroscopy (LIMIS), is first tested in all-solid-state photovoltaic cells by recording the individual photocurrent (IMPS) and photovoltage (IMVS) responsivity signals due to a small light perturbation at open-circuit (OC), and combining them: LIMIS=IMVS/IMPS. The experimental LIMIS spectra from silicon, organic, and perovskite solar cells are presented and compared with IS. An analysis of the equivalent circuit numerical models for total resistive and capacitive features is discussed. Our theoretical findings show a correction to the lifetimes evaluations by obtaining the total differential resistances and capacitances combining IS and LIMIS measurements. This correction addresses the discrepancies among different techniques, as shown with transient photovoltage. The experimental differences between IS and LIMIS (i) proves the unviability of the superposition principle, (ii) suggest a bias-dependent photo-current correction to the empirical Shockley equation of the steady-state current at different illumination intensities around OC and (iii) are proposed as a potential figure of merit for characterizing performance and stability of solar cells. In addition, new features are reported for the low-frequency capacitance of perovskite solar cells, measured by IS and LIMIS.

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards multi-component active layer blends. We report the development of high-throughput and autonomous experimentation methods for the effective optimization of multi-component polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a four-dimensional parameter space of quaternary OPV blends is mapped and optimized for photo-stability. While with conventional approaches roughly 100 mg of material would be necessary, the robot based platform can screen 2,000 combinations with less than 10 mg and machine learning enabled autonomous experimentation identifies the stable compositions with less than 1 mg.

Process optimization of photovoltaic devices is a time-intensive, trial and error endeavor, without full transparency of the underlying physics, and with user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach that identifies the root cause(s) of underperformance with layer by-layer resolution and reveals alternative optimal process windows beyond global black-box optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layer-by-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.