Autonomous chemical science and engineering enabled by self-driving laboratories

AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high-throughput solution for nutritional analysis.

Critical Mineral Separations: Opportunities for Membrane Materials and Processes to Advance Sustainable Economies and Secure Supplies

Sustainable energy solutions and electrification are driving increased demand for critical minerals. Unfortunately, current mineral processing techniques are resource intensive, use large quantities of hazardous chemicals, and occur at centralized facilities to realize economies of scale. These aspects of existing technologies are at odds with the sustainability goals driving increased demand for critical minerals. Here, we argue that the small footprint and modular nature of membrane technologies position them well to address declining concentrations in ores and brines, the variable feed concentrations encountered in recycling, and the environmental issues associated with current separation processes; thus, membrane technologies provide new sustainable pathways to strengthening resilient critical mineral supply chains. The success of creating circular economies hinges on overcoming diverse barriers across the molecular to infrastructure scales. As such, solving these challenges requires the convergence of research across disciplines rather than isolated innovations.

Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration normally remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., 1215 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevented degradation of drug. Moreover, the high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®.

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.

Perspectives for self-driving labs in synthetic biology

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

A versatile non-fouling multi-step flow reactor platform: demonstration for partial oxidation synthesis of iron oxide nanoparticles

In the last decade flow reactors for material synthesis were firmly established, demonstrating advantageous operating conditions, reproducible and scalable production via continuous operation, as well as high-throughput screening of synthetic conditions. Reactor fouling, however, often restricts flow chemistry and the common fouling prevention via segmented flow comes at the cost of inflexibility. Often, the difficulty of feeding reagents into liquid segments (droplets or slugs) constrains flow syntheses using segmented flow to simple synthetic protocols with a single reagent addition step prior or during segmentation. Hence, the translation of fouling prone syntheses requiring multiple reagent addition steps into flow remains challenging. This work presents a modular flow reactor platform overcoming this bottleneck by fully exploiting the potential of three-phase (gas-liquid-liquid) segmented flow to supply reagents after segmentation, hence facilitating fouling free multi-step flow syntheses. The reactor design and materials selection address the operation challenges inherent to gas-liquid-liquid flow and reagent addition into segments allowing for a wide range of flow rates, flow ratios, temperatures, and use of continuous phases (no perfluorinated solvents needed). This "Lego®-like" reactor platform comprises elements for three-phase segmentation and sequential reagent addition into fluid segments, as well as temperature-controlled residence time modules that offer the flexibility required to translate even complex nanomaterial synthesis protocols to flow. To demonstrate the platform's versatility, we chose a fouling prone multi-step synthesis, i.e., a water-based partial oxidation synthesis of iron oxide nanoparticles. This synthesis required I) the precipitation of ferrous hydroxides, II) the addition of an oxidation agent, III) a temperature treatment to initiate magnetite/maghemite formation, and IV) the addition of citric acid to increase the colloidal stability. The platform facilitated the synthesis of colloidally stable magnetic nanoparticles reproducibly at well-controlled synthetic conditions and prevented fouling using heptane as continuous phase. The biocompatible particles showed excellent heating abilities in alternating magnetic fields (ILP values >3 nH m² kg_Fe^-1), hence, their potential for magnetic hyperthermia cancer treatment. The platform allowed for long term operation, as well as screening of synthetic conditions to tune particle properties. This was demonstrated via the addition of tetraethylenepentamine, confirming its potential to control particle morphology. Such a versatile reactor platform makes it possible to translate even complex syntheses into flow, opening up new opportunities for material synthesis.

Automation of yeast spot assays using an affordable liquid handling robot

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation.