A universal system for digitization and automatic execution of the chemical synthesis literature

Disordered Rocksalts as High-Energy and Earth-Abundant Li-Ion Cathodes

To address the growing demand for energy and support the shift toward transportation electrification and intermittent renewable energy, there is an urgent need for low-cost, energy-dense electrical storage. Research on Li-ion electrode materials has predominantly focused on ordered materials with well-defined lithium diffusion channels, limiting cathode design to resource-constrained Ni- and Co-based oxides and lower-energy polyanion compounds. Recently, disordered rocksalts with lithium excess (DRX) have demonstrated high capacity and energy density when lithium excess and/or local ordering allow statistical percolation of lithium sites through the structure. This cation disorder can be induced by high temperature synthesis or mechanochemical synthesis methods for a broad range of compositions. DRX oxides and oxyfluorides containing Earth-abundant transition metals have been prepared using various synthesis routes, including solid-state, molten-salt, and sol-gel reactions. This review outlines DRX design principles and explains the effect of synthesis conditions on cation disorder and short-range cation ordering (SRO), which determines the cycling stability and rate capability. In addition, strategies to enhance Li transport and capacity retention with Mn-rich DRX possessing partial spinel-like ordering are discussed. Finally, the review considers the optimization of carbon and electrolyte in DRX materials and addresses key challenges and opportunities for commercializing DRX cathodes.

Science acceleration and accessibility with self-driving labs

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs' potential to enhance human-machine and human-human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.

A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multiagent system, ChemAgents, based on an on-board Llama-3.1-70B LLM, capable of executing complex, multistep experiments with minimal human intervention. It operates through a Task Manager agent that interacts with human researchers and coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, and Robot Operator─each leveraging one of four foundational resources: a comprehensive Literature Database, an extensive Protocol Library, a versatile Model Library, and a state-of-the-art Automated Lab. We demonstrate its versatility and efficacy through six experimental tasks of varying complexity, ranging from straightforward synthesis and characterization to more complex exploration and screening of experimental parameters, culminating in the discovery and optimization of functional materials. Additionally, we introduce a seventh task, where ChemAgents is deployed in a new robotic chemistry lab environment to autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability and adaptability. Our multiagent-driven robotic AI chemist showcases the potential of on-demand autonomous chemical research to accelerate discovery and democratize access to advanced experimental capabilities across academic disciplines and industries.

Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform

Mining and expanding high-quality genetic parts for synthetic biology and bioengineering are urgent needs in the research and development of next-generation biotechnology. However, gene mining has relied on sequence homology or ample expert knowledge, which fundamentally limits the establishment of a comprehensive genetic part catalog. In this work, we propose SYMPLEX (synthetic biological part mining platform by large language model-enabled knowledge extraction), a universal gene-mining platform based on large language models. We applied SYMPLEX to mine enzymes responsible for messenger RNA (mRNA) capping, a key process in eukaryotic posttranscriptional modification, and obtained thousands of diverse candidates with traceable evidence from biomedical literature and databases. Of the 46 experimentally tested integral capping enzyme candidates, 14 demonstrated in vivo cross-species capping activity, and 2 displayed superior in vitro activity over the commercial vaccinia capping enzymes currently used in mRNA vaccine production. SYMPLEX provides a distinct paradigm for functional gene mining and offers powerful tools to facilitate knowledge discovery in fundamental research.

From text to insight: large language models for chemical data extraction

The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling non-experts to extract structured, actionable data from unstructured text efficiently. While applying LLMs to chemical and materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This tutorial review provides a comprehensive overview of LLM-based structured data extraction in chemistry, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and chemical expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven chemical research. The insights presented here could significantly enhance how researchers across chemical disciplines access and utilize scientific information, potentially accelerating the development of novel compounds and materials for critical societal needs.

Reaction blueprints and logical control flow for parallelized chiral synthesis in the Chemputer

Despite recent proliferation of programmable robotic chemistry hardware, current chemical programming ontologies lack essential structured programming constructs like variables, functions, and loops. Herein we present an integration of these concepts into χDL, a universal high-level chemical programming language executable in the Chemputer. To achieve this, we introduce reaction blueprints as a chemical analog to functions in computer science, allowing to apply sets of synthesis operations to different reagents and conditions. We further expand χDL with logical operation queues and iteration via pattern matching. The combination of these new features allows encoding of chemical syntheses in generalized, reproducible, and parallelized digital workflows rather than opaque and entangled single-step operations. This is showcased by synthesizing chiral diarylprolinol catalysts and subsequently utilizing them in various synthetic transformations (13 separate automated runs affording 3 organocatalysts and 12 distinct enantioenriched products in 42-97% yield, up to > 99:1 er), including automated catalyst recycling and reuse.

Algorithm-Driven Robotic Discovery of Polyoxometalate-Scaffolding Metal-Organic Frameworks

The experimental exploration of the chemical space of crystalline materials, especially metal-organic frameworks (MOFs), requires multiparameter control of a large set of reactions, which is unavoidably time-consuming and labor-intensive when performed manually. To accelerate the rate of material discovery while maintaining high reproducibility, we developed a machine learning algorithm integrated with a robotic synthesis platform for closed-loop exploration of the chemical space for polyoxometalate-scaffolding metal-organic frameworks (POMOFs). The eXtreme Gradient Boosting (XGBoost) model was optimized by using updating data obtained from the uncertainty feedback experiments and a multiclass classification extension based on the POMOF classification from their chemical constitution. The digital signatures for the robotic synthesis of POMOFs were represented by the universal chemical description language (χDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs including one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered with a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8. Furthermore, the electrochemical properties of the synthesized POMOFs indicate superior electron transfer compared to the molecular POMs and the direct effect of the ratio of Zn, the type of ligands used, and the topology structures in POMOFs for modulating electron transfer abilities.

Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day

The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Extracting structured data from organic synthesis procedures using a fine-tuned large language model

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD "messages" (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.

Improving reproducibility through condition-based sensitivity assessments: application, advancement and prospect

The fluctuating reproducibility of scientific reports presents a well-recognised issue, frequently stemming from insufficient standardisation, transparency and a lack of information in scientific publications. Consequently, the incorporation of newly developed synthetic methods into practical applications often occurs at a slow rate. In recent years, various efforts have been made to analyse the sensitivity of chemical methodologies and the variation in quantitative outcome observed across different laboratory environments. For today's chemists, determining the key factors that really matter for a reaction's outcome from all the different aspects of chemical methodology can be a challenging task. In response, we provide a detailed examination and customised recommendations surrounding the sensitivity screen, offering a comprehensive assessment of various strategies and exploring their diverse applications by research groups to improve the practicality of their methodologies.

A chemical reaction entity recognition method based on a natural language data augmentation strategy

Impressive applications of artificial intelligence in the field of chemical reaction prediction heavily depend on abundant reliable datasets. The automated extraction of reaction procedures to build structured chemical databases is of growing importance. Here, we propose a novel model named DACRER for large-scale reaction extraction, in which transfer learning and a data augmentation strategy were employed. This model was evaluated for chemical datasets and shows good performance in identifying and processing chemical texts.

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.

Automation and machine learning augmented by large language models in a catalysis study

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

Fine-tuning large language models for chemical text mining

Extracting knowledge from complex and diverse chemical texts is a pivotal task for both experimental and computational chemists. The task is still considered to be extremely challenging due to the complexity of the chemical language and scientific literature. This study explored the power of fine-tuned large language models (LLMs) on five intricate chemical text mining tasks: compound entity recognition, reaction role labelling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance spectroscopy (NMR) data extraction, and the conversion of reaction paragraphs to action sequences. The fine-tuned LLMs demonstrated impressive performance, significantly reducing the need for repetitive and extensive prompt engineering experiments. For comparison, we guided ChatGPT (GPT-3.5-turbo) and GPT-4 with prompt engineering and fine-tuned GPT-3.5-turbo as well as other open-source LLMs such as Mistral, Llama3, Llama2, T5, and BART. The results showed that the fine-tuned ChatGPT models excelled in all tasks. They achieved exact accuracy levels ranging from 69% to 95% on these tasks with minimal annotated data. They even outperformed those task-adaptive pre-training and fine-tuning models that were based on a significantly larger amount of in-domain data. Notably, fine-tuned Mistral and Llama3 show competitive abilities. Given their versatility, robustness, and low-code capability, leveraging fine-tuned LLMs as flexible and effective toolkits for automated data acquisition could revolutionize chemical knowledge extraction.

A materials terminology knowledge graph automatically constructed from text corpus

A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters

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.

Automated, Capsule-Based Suzuki-Miyaura Cross Couplings

The development of an automated process for Suzuki-Miyaura cross couplings is described, in which the complete reaction, workup, and product isolation are effected automatically with no user involvement, aside from loading of the starting materials and reaction capsule. This practical and simple method was successfully demonstrated to provide the desired biaryl products using a range of aryl bromides and boronic acids and is also effective for the late-stage functionalization of aryl halides in bioactive molecules.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

An integrated self-optimizing programmable chemical synthesis and reaction engine

Robotic platforms for chemistry are developing rapidly but most systems are not currently able to adapt to changing circumstances in real-time. We present a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, we demonstrate the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures. We also show how the use of in-line spectroscopy such as HPLC, Raman, and NMR can be used for closed-loop optimization of reactions, exemplified using Van Leusen oxazole synthesis, a four-component Ugi condensation and manganese-catalysed epoxidation reactions, as well as two previously unreported reactions, discovered from a selected chemical space, providing up to 50% yield improvement over 25-50 iterations. Finally, we demonstrate an experimental pipeline to explore a trifluoromethylations reaction space, that discovers new molecules.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

A dynamic knowledge graph approach to distributed self-driving laboratories

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model's adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

A robotic AI-Chemist system for multi-modal AI-ready database

By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.

Difference of Convex Functions Programming With Machine-Learning Prior for the Imaging Problem in Electrical Capacitance Tomography

The electrical capacitance tomography technology has potential benefits for the process industry by providing visualization of material distributions. One of the main technical gaps and impediments that must be overcome is the low-quality tomogram. To address this problem, this study introduces the data-guided prior and combines it with the electrical measurement mechanism and the sparsity prior to produce a new difference of convex functions programming problem that turns the image reconstruction problem into an optimization problem. The data-guided prior is learned from a provided dataset and captures the details of imaging targets since it is a specific image. A new numerical scheme that allows a complex optimization problem to be split into a few less difficult subproblems is developed to solve the challenging difference of convex functions programming problem. A new dimensionality reduction method is developed and combined with the relevance vector machine to generate a new learning engine for the forecast of the data-guided prior. The new imaging method fuses multisource information and unifies data-guided and measurement physics modeling paradigms. Performance evaluation results have validated that the new method successfully works on a series of test tasks with higher reconstruction quality and lower noise sensitivity than the popular imaging methods.

OSPAR: A Corpus for Extraction of Organic Synthesis Procedures with Argument Roles

There is a pressing need for the automated extraction of chemical reaction information because of the rapid growth of scientific documents. The previously reported works in the literature for the procedure extraction either (a) did not consider the semantic relations between the action and argument or (b) defined a detailed schema for the extraction. The former method was insufficient for reproducing the reaction, while the latter methods were too specific to their own schema and did not consider the general semantic relation between the verb and argument. In addition, they did not provide an annotated text that aligned with the structured procedure. Along these lines, in this work, we propose a corpus named organic synthesis procedures with argument roles (OSPAR) that is annotated with rolesets to consider the semantic relation between the verb and argument. We also provide rolesets for chemical reactions, especially for organic synthesis, which represent the argument roles of actions in the corpus. More specifically, we annotated 112 organic synthesis procedures in journal articles from Organic Syntheses and defined 19 new rolesets in addition to 29 rolesets from an existing language resource (Proposition Bank). After that, we constructed a simple deep learning system trained on OSPAR and discussed the usefulness of the corpus by comparing it with chemical description language (XDL) generated by a natural language processing tool, namely, SynthReader. While our system's output required more detailed parsing, it covered comparable information against XDL. Moreover, we confirmed that the validation of the output action sequence was easy as it was aligned with the original text.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology

AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Before leveraging big data methods like machine learning and artificial intelligence (AI) in chemistry, there is an imperative need for an affordable, universal digitization standard. This mirrors the foundational requisites of the digital revolution, which demanded standard architectures with precise specifications. Recently, we have developed automated platforms tailored for chemical AI-driven exploration, including the synthesis of molecules, materials, nanomaterials, and formulations. Our focus has been on designing and constructing affordable standard hardware and software modules that serve as a blueprint for chemistry digitization across varied fields. Our platforms can be categorized into four types based on their applications: (i) discovery systems for the exploration of chemical space and novel reactivity, (ii) systems for the synthesis and manufacture of fine chemicals, (iii) platforms for formulation discovery and exploration, and (iv) systems for materials discovery and synthesis. We also highlight the convergent evolution of these platforms through shared hardware, firmware, and software alongside the creation of a unique programming language for chemical and material systems. This programming approach is essential for reliable synthesis, designing experiments, discovery, optimization, and establishing new collaboration standards. Furthermore, it is crucial for verifying literature findings, enhancing experimental outcome reliability, and fostering collaboration and sharing of unsuccessful experiments across different research labs.

Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language

Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage their historical data in ML development. Here we show that a domain specific language, termed Chemical Markdown Language (CMDL), provides flexible, extensible, and consistent representation of disparate experiment types and polymer structures. CMDL enables seamless use of historical experimental data to fine-tune regression transformer (RT) models for generative molecular design tasks. We demonstrate the utility of this approach through the generation and the experimental validation of catalysts and polymers in the context of ring-opening polymerization-although we provide examples of how CMDL can be more broadly applied to other polymer classes. Critically, we show how the CMDL tuned model preserves key functional groups within the polymer structure, allowing for experimental validation. These results reveal the versatility of CMDL and how it facilitates translation of historical data into meaningful predictive and generative models to produce experimentally actionable output.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

Next-generation intelligent laboratories for materials design and manufacturing

The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry

Library generation experiments are a key part of the discovery of new materials, methods, and models in chemistry, but the question of how to generate high quality libraries to enable discovery is nontrivial. Herein, we use coordination chemistry to demonstrate the automation of many of the workflows used for library generation in automated hardware including the Chemputer. First, we explore the target-oriented synthesis of three influential coordination complexes, to validate key synthetic operations in our system; second, the generation of focused libraries in chemical and process space; and third, the development of a new workflow for prospecting library formation. This involved Bayesian optimization using a Gaussian process as surrogate model combined with a metric for novelty (or serendipity) quantification based on mass spectrometry data. In this way, we show directed exploration of a process space toward those areas with rarer observations and build a picture of the diversity in product distributions present across the space. We show that this effectively "engineers" serendipity into our search through the unexpected appearance of acetic anhydride, formed in situ, and solvent degradation products as ligands in an isolable series of three Co(III) anhydride complexes.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

A vending machine for drug-like molecules - automated synthesis of virtual screening hits

As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 10¹² members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.

A perspective on automated advanced continuous flow manufacturing units for the upgrading of biobased chemicals toward pharmaceuticals

Biomass is a renewable, almost infinite reservoir of a large diversity of highly functionalized chemicals. The conversion of biomass toward biobased platform molecules through biorefineries generally still lacks economic viability. Profitability could be enhanced through the development of new market opportunities for these biobased platform chemicals. The fine chemical industry, and more specifically the manufacturing of pharmaceuticals is one of the sectors bearing significant potential for these biobased building blocks to rapidly emerge and make a difference. There are, however, still many challenges to be dealt with before this market can thrive. Continuous flow technology and its integration for the upgrading of biobased platform molecules for the manufacturing of pharmaceuticals is foreseen as a game-changer. This perspective reflects on the main challenges relative to chemical, process, regulatory and supply chain-related burdens still to be addressed. The implementation of integrated continuous flow processes and their automation into modular units will help for tackling with these challenges.

Graphical abstract

The way to AI-controlled synthesis: how far do we need to go?

Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.

An AI-Based Curling Game System for Winter Olympics

The real-time application of artificial intelligence (AI) technologies in sports is a long-standing challenge owing to large spatial sports field, complexity, and uncertainty of real-world environment, etc. Although some AI-based systems have been applied to sporting events such as tennis, basketball, and football, they are replayed after the game rather than applied in real time. Here, we present an AI-based curling game system, termed CurlingHunter, which can display actual trajectories, predicted trajectories, and house regions of curling during the games via a giant screen in curling stadiums and a live streaming media platform on the internet in real time, so as to assist the game, improve the interest of watching game, help athletes train, etc. We provide a complete description of CurlingHunter' architecture and a thorough evaluation of its performances and demonstrate that CurlingHunter possesses remarkable real-time performance (~9.005 ms), high accuracy (30 ± 3 cm under measurement distance > 20 m), and good stability. CurlingHunter is the first, to the best of our knowledge, real-time system that can assist athletes to compete during the games in the history of sports and has been successfully applied in Winter Olympics and Winter Paralympics. Our work highlights the potential of AI-based systems for real-time applications in sports.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials

We present an autonomous chemical synthesis robot for the exploration, discovery, and optimization of nanostructures driven by real-time spectroscopic feedback, theory, and machine learning algorithms that control the reaction conditions and allow the selective templating of reactions. This approach allows the transfer of materials as seeds between cycles of exploration, opening the search space like gene transfer in biology. The open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line ultraviolet-visible characterization led to the discovery of five categories of nanoparticles by only performing ca. 1000 experiments in three hierarchically linked chemical spaces. The platform optimized nanostructures with desired optical properties by combining experiments and extinction spectrum simulations to achieve a yield of up to 95%. The synthetic procedure is outputted in a universal format using the chemical description language (χDL) with analytical data to produce a unique digital signature to enable the reproducibility of the synthesis.

An all-round AI-Chemist with a scientific mind

The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly. It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14 workstations, including synthesis, characterization and performance tests. The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization, allowing a new hypothesis for the next iteration to be proposed. The competence of the AI-Chemist has been scrutinized by three different chemical tasks. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory.

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

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.