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

ConspectusIn 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 high-throughput 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 semiconductor composites, the SPINBOT system, which is a spin-coating DAP with the potential to optimize complex device architectures, and finally, AMANDA, a fully integrated and autonomously operating DAP. Notably, we underscore the utilization of a robot-based high-throughput experimentation technique to address the common optimization challenges encountered in extensive multidimensional composition and parameter spaces pertaining to organic and perovskite photovoltaics materials. Finally, we briefly propose a holistic concept and technology, a self-driven autonomous material and device acceleration platform (AMADAP) laboratory, for autonomous functional solar materials discovery and development. We hope to discover how AMADAP can be further strengthened and universalized with advancing development of hardware and software infrastructures in the future.

Maximizing Performance and Stability of Organic Solar Cells at Low Driving Force for Charge Separation.

Thanks to the development of novel electron acceptor materials, the power conversion efficiencies (PCE) of organic photovoltaic (OPV) devices are now approaching 20%. Further improvement of PCE is complicated by the need for a driving force to split strongly bound excitons into free charges, causing voltage losses. This review discusses recent approaches to finding efficient OPV systems with minimal driving force, combining near unity quantum efficiency (maximum short circuit currents) with optimal energy efficiency (maximum open circuit voltages). The authors discuss apparently contradicting results on the amount of exciton binding in recent literature, and approaches to harmonize the findings. A comprehensive view is then presented on motifs providing a driving force for charge separation, namely hybridization at the donor:acceptor interface and polarization effects in the bulk, of which quadrupole moments (electrostatics) play a leading role. Apart from controlling the energies of the involved states, these motifs also control the dynamics of recombination processes, which are essential to avoid voltage and fill factor losses. Importantly, all motifs are shown to depend on both molecular structure and process conditions. The resulting high dimensional search space advocates for high throughput (HT) workflows. The final part of the review presents recent HT studies finding consolidated structure-property relationships in OPV films and devices from various deposition methods, from research to industrial upscaling.

Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches.

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 the 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 "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-electronic 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.

Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine-Learning-Driven High-Throughput Platform.

Organic solar cells (OSCs) now approach power conversion efficiencies of 20%. However, in order to enter mass markets, problems in upscaling and operational lifetime have to be solved, both concerning the connection between processing conditions and active layer morphology. Morphological studies supporting the development of structure-process-property relations are time-consuming, complex, and expensive to undergo and for which statistics, needed to assess significance, are difficult to be collected. This work demonstrates that causal relationships between processing conditions, morphology, and stability can be obtained in a high-throughput method by combining low-cost automated experiments with data-driven analysis methods. An automatic spectral modeling feeds parametrized absorption data into a feature selection technique that is combined with Gaussian process regression to quantify deterministic relationships linking morphological features and processing conditions with device functionality. The effect of the active layer thickness and the morphological order is further modeled by drift-diffusion simulations and returns valuable insight into the underlying mechanisms for improving device stability by tuning the microstructure morphology with versatile approaches. Predicting microstructural features as a function of processing parameters is decisive know-how for the large-scale production of OSCs.

Highly Luminescent Transparent Cs2Ag x Na1-x Bi y In1-y Cl6 Perovskite Films Produced by Single-Source Vacuum Deposition.

Thermal deposition of halide perovskites as a universal and scalable route to transparent thin films becomes highly challenging in the case of lead-free double perovskites, requiring the evaporation dynamics of multiple metal halide sources to be balanced or a single-phase precursor preliminary synthesized to achieve a reliable control over the composition and the phase of the final films. In the present Letter, the feasibility of the single-source vacuum deposition of microcrystalline Cs2Ag x Na1-x Bi y In1-y Cl6 double perovskites into corresponding transparent nanocrystalline films while preserving the bulk spectral and structural properties is shown. The perovskite films produced from the most emissive powders with x = 0.40 and y = 0.01 revealed a photoluminescence quantum yield of 85%, highlighting thermal evaporation as a promising approach to functional perovskite-based optical materials.

Doping/Alloying Pathways to Lead-Free Halide Perovskites with Ultimate Photoluminescence Quantum Yields.

Tailored modifications of halide lead-free perovskites (LFPs) via doping/alloying with metal cations have been recognized as a promising pathway to highly efficient inorganic phosphors with photoluminescence (PL) quantum yields of up to 100 %. Such materials typically display selective sensitivity to UV light, a broad PL range, and long PL lifetimes as well as a unique compositional variability and stability-an ideal combination for many light-harvesting applications. This Minireview presents the state-of-the-art in doped LFPs, focusing on the reports published mostly in the last two to three years. We discuss the factors determining the efficiency and spectral parameters of the broadband PL of doped LFPs depending on the dopant and host matrix, both in micro- and nanocrystalline states, address the most relevant challenges this rapidly developing research area is facing, and outline the most promising concepts for further progress in this field.

The role of innovation for economy and sustainability of photovoltaic modules.

The role of innovation for the success of photovoltaics cannot be overstated. Photovoltaics have enjoyed the most substantial price learning of any energy technology. Innovation affects photovoltaic performance in more ways, though. Here, we explore the role of innovation for economics and greenhouse gas savings of photovoltaic modules using replacement scenarios. We find that the greenhouse gas displacement potential of photovoltaic modules has improved substantially over the last 20 years-4-fold for the presented example. We show that the economically ideal time for repowering is after around 20 years, but that repowering may reduce greenhouse gas savings. Expanding photovoltaic installations is generally preferable, economically and sustainably, to repowering. We argue that i) we should maximize the greenhouse gas saving potential of each module, which requires a global strategy, ii) tandem solar cells should aim for stability, and iii) efforts to continue and accelerate innovation in photovoltaic technology are needed.

Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High-Throughput Engineering Platform.

An important step of the great achievement of organic solar cells in power conversion efficiency is the development of low-band gap polymer donors, PBDB-T derivatives, which present interesting aggregation effects dominating the device performance. The aggregation of polymers can be manipulated by a series of variables from a materials design and processing conditions perspective; however, optimization of film quality is a time- and energy-consuming work. Here, we introduce a robot-based high-throughput platform (HTP) that is offering automated film preparation and optical spectroscopy thin-film characterization in combination with an analysis algorithm. PM6 films are prepared by the so-called spontaneous film spreading (SFS) process, where a polymer solution is coated on a water surface. Automated acquisition of UV/Vis and photoluminescence (PL) spectra and automated extraction of morphological features is coupled to Gaussian Process Regression to exploit available experimental evidence for morphology optimization but also for hypothesis formulation and testing with respect to the underlying physical principles. The integrated spectral modeling workflow yields quantitative microstructure information by distinguishing amorphous from ordered phases and assesses the extension of amorphous versus the ordered domains. This research provides an easy to use methodology to analyze the exciton coherence length in conjugated semiconductors and will allow to optimize exciton splitting in thin film organic semiconductor layers as a function of processing.

Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.

The Impact of COVID-19-Related Measures on the Solar Resource in Areas with High Levels of Air Pollution.

Restrictions enacted to reduce the spreading of COVID-19 have resulted in notably clearer skies around the world. In this study, we confirm that reduced levels of air pollution correlate with unusually high levels of clear-sky insolation in Delhi, India. Restrictions here were announced on March 19th, with the nation going into lockdown on March 24th. Comparing insolation data before and after these dates with insolation from previous years (2017 to 2019), we observe an 8.3% ± 1.7% higher irradiance than usual in late March and a 5.9% ± 1.6% higher one in April, while we find no significant differences in values from previous years in February or early March. Using results from a previous study, we calculated the expected increase in insolation based on measured PM2.5 concentration levels. Measurements and calculations agree within confidence intervals, suggesting that reduced pollution levels are a major cause for the observed increase in insolation.

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.

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 toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent 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 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.