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Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by iteratively rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three polymer datasets and compare them to common molecular representations. Data augmentation does not yield significant improvements in machine learning property prediction performance compared to equivalent (non-augmented) representations. In datasets where the target property is primarily influenced by the polymer sequence rather than experimental parameters, this data augmentation technique provides molecular embedding with more information to improve property prediction accuracy.

Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development., (© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed.In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki-Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction.While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 10 9 -10 11 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving laboratories. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization) and to simulate a self-driving laboratory that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ∼3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization and especially in downselecting promising candidates from a large chemical space via an a priori estimation of the synthetic costs., Competing Interests: The authors declare the following competing financial interest(s): A.A.-G. is co-founder and Chief Visionary Officer of Kebotix, Inc., (© 2022 The Authors. Published by American Chemical Society.)

Understanding the relationship between molecular structure and solid-state arrangement informs about the design of new organic semiconductor (OSC) materials with improved optoelectronic properties. However, determining their atomic structure remains challenging. Here, we report the lattice organization of two non-fullerene acceptors (NFAs) determined using microcrystal electron diffraction (MicroED) from crystals not traceable by X-ray crystallography. The MicroED structure of o-IDTBR was determined from a powder without crystallization, and a new polymorph of ITIC-Th is identified with the most distorted backbone of any NFA. Electronic structure calculations elucidate the relationships between molecular structures, lattice arrangements, and charge-transport properties for a number of NFA lattices. The high dimensionality of the connectivity of the 3D wire mesh topology is the best for robust charge transport within NFA crystals. However, some examples suffer from uneven electronic coupling. MicroED combined with advanced electronic structure modeling is a powerful new approach for structure determination, exploring polymorphism and guiding the design of new OSCs and NFAs., Competing Interests: The authors declare no competing financial interest. The data supporting the results of this work are available from the authors upon reasonable request. The structure factors and coordinates for the o-IDTBR (2033974) and ITIC-Th (2033973) structures have been deposited in the CCDC.

Precise doping of organic semiconductors allows control over the conductivity of these materials, an essential parameter in electronic applications. Although Lewis acids have recently shown promise as dopants for solution-processed polymers, their doping mechanism is not yet fully understood. In this study, we found that B(C 6 F 5 ) 3 is a superior dopant to the other Lewis acids investigated (BF 3 , BBr 3 and AlCl 3 ). Experiments indicate that Lewis acid-base adduct formation with polymers inhibits the doping process. Electron-nuclear double-resonance and nuclear magnetic resonance experiments, together with density functional theory, show that p-type doping occurs by generation of a water-Lewis acid complex with substantial Brønsted acidity, followed by protonation of the polymer backbone and electron transfer from a neutral chain segment to a positively charged, protonated one. This study provides insight into a potential path for protonic acid doping and shows how trace levels of water can transform Lewis acids into powerful Brønsted acids.

A detailed study of a high-k fluoropolymer gate dielectric material, poly(vinylidene fluoride- co-hexafluoropropylene) [P(VDF-HFP)], is presented as a guide to achieve low operational voltage and electrically stable device performance. The large dipole moment of C-F dipoles in P(VDF-HFP) is responsible for its high dielectric constant as well as its potentially ferroelectric behavior that must be minimized to avoid hysteretic current-voltage characteristics. A range of material grades and processing conditions are explored and are shown to have a significant effect on the degree of hysteresis observed in device-transfer characteristics. The percentage of HFP monomer in the P(VDF-HFP) dielectric has an effect on gate-dependent mobility induced by disorder at the semiconductor-dielectric interface. Most importantly, we present the considerations that must be made to achieve optimal performance in multiple device architectures of organic field-effect transistors when using P(VDF-HFP) as a dielectric layer.

Two conjugated oligoelectrolytes (COEs), WMG1 and WMG2, were designed with the goal of achieving near infrared absorption and high photothermal conversion efficiency. Specifically, electron-rich thiophene and electron-poor benzo[1,2-c:4,5-c']bis[1,2,5]thiadiazole subunits were introduced into the conjugated core to modulate the optical gap and to reduce the fluorescence emission efficiency. WMG1 and WMG2 show absorption maxima at around 800 nm, which favors tissue penetration. Although relatively small in size, WMG1 and WMG2 exhibit photothermal conversion efficiencies of circa 60 % and 54 %, respectively. WMG1 shows dark toxicity to the Gram positive bacterium B. subtilis and good photothermal killing efficiency toward both B. subtilis and Gram negative E. coli, features that demonstrate the promising potential of the COE molecular design for photothermal applications., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Three cyclopentadithiophene-difluorophenylene copolymers (named PhF2,3, PhF2,5, and PhF2,6), which differ by the arrangement of fluorines on the phenylene structural unit, were designed and synthesized for the fabrication of organic field-effect transistors (OFETs). Single crystal structures of model compounds representative of the backbone and density functional theory (DFT) were used to estimate the backbone shape for each copolymer. The different substitution arrangements impact the backbone secondary structure through different nonbonding F···H interactions. PhF2,5 and PhF2,6 assumed more linear backbones relative to PhF2,3, which in turn impacts self-assembly and polymer chain alignment on nanogrooved (NG) substrates. A larger improvement of charge carrier mobility for the more linear backbones was achieved when using NG substrates. Among the three polymers, PhF2,6 achieved the highest average field-effect hole mobility (5.1 cm 2 V -1 s -1 ). As evidenced by grazing incidence wide-angle X-ray scattering (GIWAXS), thin films of PF2,5 and PF2,6 exhibited a higher degree of anisotropic alignment, relative to the more curved PF2,3 counterpart, consistent with the higher hole mobilities. This work gives insight into how nonbonding interactions can influence charge carrier mobility through changes in secondary structure and suggests that polymers with more linear shapes might be preferred for achieving greater levels of alignment within the confines of a NG environment.

Two electronically delocalized molecules were designed as models to understand how molecular shape impacts the tradeoff between solubility and crystallization tendencies in molecular semiconductors. The more soluble compound TT contains a non-planar bithiophene central fragment, whereas CT has a planar cyclopentadithiophene unit. Calorimetry studies show that CT can crystallize more easily than TT. However, absorption spectroscopy shows that the initially amorphous TT film can eventually form crystals in which the molecular shape is significantly more planar. Two thermally reversible polymorphs for TT were observed by XRD and grazing-incidence wide-angle X-ray scattering (GIWAXS) measurements. These findings are relevant within the context of designing soft semiconductors that exhibit high solubility and a tendency to provide stable organized structures with desirable electronic properties., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The high temperature performance oforganic field-effect transistorsbased on a molecular organic semiconductor with intermediate dimensions, namely X2, is evaluated. Hole mobility is stable, even at 200-250 °C. Changes in device characteristics at high temperature are reversible across multiple cycles of high temperature operation. Measurements at high temperature exhibit larger hysteresis, while at low temperature one observes the emergence of ambipolar transport., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Synthesis, structural and characterization data are provided for Pt(II) and Ir(III) complexes cyclometalated with 2-(corannulene)pyridine (corpy), (corpy)Pt(dpm) and (corpy)Ir(ppz)2 (dpm = dipivaloylmethanato, ppz = 1-phenylpyrazolyl). A third compound, (phenpy)Ir(ppz)2 (phenpy = 2-(5-phenanthryl)pyridyl), was also prepared to mimic the steric bulk of (corpy)Ir(ppz)2. X-ray analysis reveals bowl depths of 0.895 Å for (corpy)Pt(dpm) and 0.837 Å in (corpy)Ir(ppz)2. Neither complex displayed bowl-to-bowl stacking in the crystal lattice. A fluxional process for (corpy)Ir(ppz)2 attributed to bowl inversion of corrannulene is observed in solution with a barrier (ΔG(‡) = 13 kcal mol(-1)) and rate (k = 2.5 × 10(3) s(-1)) as determined using variable temperature (1)H NMR spectroscopy. All of the complexes display red phosphorescence at room temperature with quantum yields of 0.05 in solution and 0.2 in polymethyl methacrylate (PMMA).

Helium nanodroplets are considered ideal model systems to explore quantum hydrodynamics in self-contained, isolated superfluids. However, exploring the dynamic properties of individual droplets is experimentally challenging. In this work, we used single-shot femtosecond x-ray coherent diffractive imaging to investigate the rotation of single, isolated superfluid helium-4 droplets containing ~10(8) to 10(11) atoms. The formation of quantum vortex lattices inside the droplets is confirmed by observing characteristic Bragg patterns from xenon clusters trapped in the vortex cores. The vortex densities are up to five orders of magnitude larger than those observed in bulk liquid helium. The droplets exhibit large centrifugal deformations but retain axially symmetric shapes at angular velocities well beyond the stability range of viscous classical droplets., (Copyright © 2014, American Association for the Advancement of Science.)