A Materials Acceleration Platform for Organic Laser Discovery

Self-Driving Laboratories for Chemistry and Materials Science

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.

Scientific Discovery Framework Accelerating Advanced Polymeric Materials Design

Organic polymer materials, as the most abundantly produced materials, possess a flammable nature, making them potential hazards to human casualties and property losses. Target polymer design is still hindered due to the lack of a scientific foundation. Herein, we present a robust, generalizable, yet intelligent polymer discovery framework, which synergizes diverse capabilities, including the in situ burning analyzer, virtual reaction generator, and material genomic model, to achieve results that surpass the sum of individual parts. Notably, the high-throughput analyzer created for the first time, grounded in multiple spectroscopic principles, enables in situ capturing of massive combustion intermediates; then, the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information; further, the proposed feature engineering tool, which embedded both polymer hierarchical structures and massive intermediate data, develops the generalizable genomic model with excellent universality (adapting over 20 kinds of polymers) and high accuracy (88.8%), succeeding discovering series of novel polymers. This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.

Using scalable computer vision to automate high-throughput semiconductor characterization

High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1-xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

The relevance of sustainable laboratory practices

Scientists are of key importance to the society to advocate awareness of the climate crisis and its underlying scientific evidence and provide solutions for a sustainable future. As much as scientific research has led to great achievements and benefits, traditional laboratory practices come with unintended environmental consequences. Scientists, while providing solutions to climate problems and educating the young innovators of the future, are also part of the problem: excessive energy consumption, (hazardous) waste generation, and resource depletion. Through their own research operations, science, research and laboratories have a significant carbon footprint and contribute to the climate crisis. Climate change requires a rapid response across all sectors of society, modeled by inspiring leaders. A broader scientific community that takes concrete actions would serve as an important step in convincing the general public of similar actions. Over the past years, grassroots movements across the sciences have recognized the overlooked impact of the scientific enterprise, and so-called Green Lab initiatives emerged seeking to address the environmental footprint of research. Driven by the voluntary efforts of researchers and staff, they educate peers, develop sustainability guidelines, write scientific publications and maintain accreditation frameworks. With this perspective we want to advocate for and spark leadership to promote a systemic change in laboratory practices and approach to research. Comprehensive evidence for the environmental impact of laboratories and their root-causes is presented, expanded with data from a current case study of the University of Groningen showcasing annual savings of 398 763 € as well as 477.1 tons of CO2e. This is followed by guidelines for sustainable lab practices and hands-on advice on how to achieve a systemic change at research institutions and industry. How can we expect industry, politics, and society to change, if we as scientists are not changing either? Scientists should lead by example and practice the change they want to see.

Progress and Perspective toward Continuous-Wave Organic Solid-State Lasers

A continuous-wave (CW) organic solid-state laser is highly desirable for spectroscopy, sensing, and communications, but is a significant challenge in optoelectronics. The accumulation of long-lived triplet excitons and relevant excited-state absorptions, as well as singlet-triplet annihilation, are the main obstacles to CW lasing. Here, progress in singlet- and triplet-state utilizations in organic gain media is reviewed to reveal the issues in working with triplets. Then, exciton behaviors that inhibit light oscillations during long excitation pulses are discussed. Further, recent advances in increasing organic lasing pulse widths from microseconds toward the indication of CW operation are summarized with respect to molecular designs, advanced resonator architectures, triplet scavenging, and potential triplet contribution strategies. Finally, future directions and perspectives are proposed for achieving stable CW organic lasers with significant triplet contribution.

A Boron Scan of Ethyl Acetoacetate Leads to Versatile Building Blocks

Discovered in the 19th century, ethyl acetoacetate has been central to the development of organic chemistry, including its pedagogy and applications. In this study, we present borylated derivatives of this venerable molecule. A boron handle has been installed at either α ${{\rm \alpha }}$ - or β ${\beta }$ -position of acetoacetate by homologation of acyl-MIDA (N-methyliminodiacetic acid) boronates with diazoacetates. Either alkyl or boryl groups were found to migrate with regiochemistry being a function of the steric bulk of the diazo species. Boryl β ${{\rm \beta }}$ -ketoesters can be further modified into borylated pyrazolones and oximes, thereby expanding the synthetic toolkit and offering opportunities for additional modifications.

Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity

Solution-processed inorganic solar cells with less toxic and earth-abundant elements are emerging as viable alternatives to high-performance lead-halide perovskite solar cells. However, the wide range of elements and process parameters impede the rapid exploration of vast chemical spaces. Here, we developed an automated robot-embedded measurement system that performs photoabsorption spectroscopy, optical microscopy, and white-light flash time-resolved microwave conductivity (TRMC). We tested 576 films of quaternary element-blended wide-bandgap Cs-Bi-Sb-I semiconductors with various compositions, organic salt additives (MACl, FACl, MAI, and FAI, where MA and FA represent methylammonium and formamidinium, respectively), and thermal annealing temperatures. Among them, we found that the maximum power conversion efficiency (PCE) was 2.36%, which is significantly higher than the PCE of 0.68% for a reference film without an additive. Machine learning (ML) and statistical analyses revealed significant features and their relationships with TRMC transients, thereby demonstrating the advantages of combining ML and automated experiments for the high-throughput exploration of photovoltaic materials.

β-Boron Effect Enables Regioselective and Stereospecific Electrophilic Addition to Alkenes

Electrophilic addition to alkenes is a textbook-taught reaction, yet it is not always possible to control the regioselectivity of addition to unsymmetrical 1,2-disubstituted substrates. We report the observation and applications of the β-boron effect that accounts for high regioselectivity in electrophilic addition reactions to allylic MIDA (N-methyliminodiacetic acid) boronates. While the well-established β-silicon effect bears partial resemblance to the observed reactivity, the silyl group is typically lost during functionalization. In contrast, the boryl moiety is retained in the product when B(MIDA) is used as the nucleophilic stabilizer. Mechanistic studies elucidate the origin of this effect and demonstrate how σ(C-B) hyperconjugation helps stabilize the incipient carbocation. This transformation represents a rare example of the stereospecific hydrohalogenation of secondary allyl MIDA-boronates that proceeds in a syn-fashion.

High-throughput property-driven generative design of functional organic molecules

The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.