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The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general., Comment: 21 pages, 5 figures

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

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle of Like dissolves like, we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electric properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production., Comment: 18 pages, 5 figures

To identify abnormal photovoltaic (PV) modules in large-scale PV plants economically, drone-mounted infrared (IR) cameras and automated video processing algorithms are frequently used. While most related works focus on the detection of abnormal modules, little has been done to automatically localize those modules within the plant. In this work, we use incremental structure-from-motion to automatically obtain geocoordinates of all PV modules in a plant based on visual cues and the measured GPS trajectory of the drone. In addition, we extract multiple IR images of each PV module. Using our method, we successfully map 99.3 % of the 35084 modules in four large-scale and one rooftop plant and extract over 2.2 million module images. As compared to our previous work, extraction misses 18 times less modules (one in 140 modules as compared to one in eight). Furthermore, two or three plant rows can be processed simultaneously, increasing module throughput and reducing flight duration by a factor of 2.1 and 3.7, respectively. Comparison with an accurate orthophoto of one of the large-scale plants yields a root mean square error of the estimated module geocoordinates of 5.87 m and a relative error within each plant row of 0.22 m to 0.82 m. Finally, we use the module geocoordinates and extracted IR images to visualize distributions of module temperatures and anomaly predictions of a deep learning classifier on a map. While the temperature distribution helps to identify disconnected strings, we also find that its detection accuracy for module anomalies reaches, or even exceeds, that of a deep learning classifier for seven out of ten common anomaly types. The software is published at https://github.com/LukasBommes/PV-Hawk.

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample train and test data from the same distribution ignoring the presence of domain shift between data of different PV plants. Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labelled data of one source PV plant and make predictions on another target plant. We train a ResNet-34 convolutional neural network with a supervised contrastive loss, on top of which we employ a k-nearest neighbor classifier to detect anomalies. Our method achieves a satisfactory area under the receiver operating characteristic (AUROC) of 73.3 % to 96.6 % on nine combinations of four source and target datasets with 2.92 million IR images of which 8.5 % are anomalous. It even outperforms a binary cross-entropy classifier in some cases. With a fixed decision threshold this results in 79.4 % and 77.1 % correctly classified normal and anomalous images, respectively. Most misclassified anomalies are of low severity, such as hot diodes and small hot spots. Our method is insensitive to hyperparameter settings, converges quickly and reliably detects unknown types of anomalies making it well suited for practice. Possible uses are in automatic PV plant inspection systems or to streamline manual labelling of IR datasets by filtering out normal images. Furthermore, our work serves the community with a more realistic view on PV module fault detection using unsupervised domain adaptation to develop more performant methods with favorable generalization capabilities.

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet-50 to classify ten common module anomalies with more than 90 % test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV installations per day, potentially scaling to multi-gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification.

Degradation of backsheets (BS) and encapsulant polymer components of silicon PV modules is recognized as one of the main reasons for losses in PV plant performance and lifetime expectations. Here, we report first insights into the correlation between BS composition of PV-modules and PV power station performance by using a combination of lab- and field-imaging, as well as spectroscopic and electrical characterizations. Using field-suitable near-infrared absorption (NIRA) spectroscopy, the BS structure of 518 PV-modules, 2.5 percent of the PV-modules in a 5 MWp PV power station, was identified on-site. The variance of the BS composition was found to be heterogeneous across PV-modules of the same power class from the same manufacturer. Polymaide-based BS cause in 10 out of 100 inverters ground impedance values below 400 kOhm, which is a typical threshold for inverters connecting to the grid. For primer-based BS this low value is reached 20 times. We conclude these numbers best as possible from the available monitoring data of inverters associated with BS-types from NIRA. Challenging is the identification of degraded primer-based BSs, since visually they look healthy and undistinguishable to well performing BSs. The present results demonstrate that a deeper understanding of the relationship between bill-of-materials and performance of PV-modules is necessary to avoid/minimize inverter shut-downs, and that it can be achieved by using combinations of selected field and lab characterization methods with monitoring data., Comment: 23 pages, 9 figures

The development of complex functional materials poses a multi-objective optimization problem in a large multidimensional parameter space. Solving it requires reproducible, user independent laboratory work and intelligent preselection of experiments. However, experimental materials science is a field where manual routines are still predominant, although other domains like pharmacy or chemistry have long used robotics and automation. As the number of publications on Materials Acceleration Platforms (MAPs) increases steadily, we review selected systems and fit them into the stages of a general material development process to examine the evolution of MAPs. Subsequently we present our approach to laboratory automation in materials science. We introduce AMANDA (Autonomous Materials and Device Application Platform), a generic platform for distributed materials research comprising a self-developed software backbone and several MAPs. One of them, LineOne (L1), is specifically designed to produce and characterize solution processed thin-film devices like organic solar cells (OSC). It is designed to perform precise closed-loop screenings of up to 272 device variations per day yet allows further upscaling. Each individual solar cell is fully characterized and all process steps are comprehensively documented. We want to demonstrate the capabilities of AMANDA L1 with OSCs based on PM6:Y6 with 13.7% efficiency when processed in air. Further we discuss challenges and opportunities of highly automated research platforms and elaborate on the future integration of additional techniques, methods and algorithms in order to advance to fully autonomous self-optimizing systems - a paradigm shift in functional materials development leading to the laboratory of the future.

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.

We have measured the velocity of a running crack in brittle single crystal silicon as a function of energy flow to the crack tip. The experiments are designed to permit direct comparison with molecular dynamics simulations; therefore the experiments provide an indirect but sensitive test of interatomic potentials. Performing molecular dynamics simulations of brittle crack motion at the atomic scale we find that experiments and simulations disagree showing that interatomic potentials are not yet well understood., Comment: 4 pages, 4 figures, 19 references