An all-round AI-Chemist with a scientific mind

Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.

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.

Intelligent design and synthesis of energy catalytic materials

Efficient energy conversion and storage are crucial for the sustainable development and growth of renewable energy sources. However, the limited varieties of traditional energy catalytic materials cannot match the fast-expansion requirement of raising various clean energy for industrial applications. Thus, accelerating the design and synthesis of high-performance catalysts is necessary for the application of energy equipment. Recently, with artificial intelligence (AI) technology being advanced by leaps and bounds, it is feasible to efficiently and precisely screen materials and optimize synthesis conditions in a huge unknown space. Here, we introduce and review AI techniques used in the development of catalytic materials in detail. We describe the workflow for designing and synthesizing new materials using machine learning (ML) and robotics. We summarize the sources of data collection, the intelligent algorithms commonly used to build ML models, and the laboratory modules for the intelligent synthesis of materials. We provide the illustrations of predicting the properties of catalytic materials with ML assistance in different material types. In addition, we present the potential strategies for finding material synthesis pathways, and advances in robotics to accelerate high-performance catalytic materials synthesis in the review. Finally, the summary, challenges, and potential directions in the development of AI-assisted catalytic materials are presented and discussed.

Monitoring C-C coupling in catalytic reactions via machine-learned infrared spectroscopy

Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C-C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO-CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.

Atomically Precise Cu Nanoclusters: Recent Advances, Challenges, and Perspectives in Synthesis and Catalytic Applications

Atomically precise metal nanoclusters are an emerging type of nanomaterial which has diverse interfacial metal-ligand coordination motifs that can significantly affect their physicochemical properties and functionalities. Among that, Cu nanoclusters have been gaining continuous increasing research attentions, thanks to the low cost, diversified structures, and superior catalytic performance for various reactions. In this review, we first summarize the recent progress regarding the synthetic methods of atomically precise Cu nanoclusters and the coordination modes between Cu and several typical ligands and then discuss the catalytic applications of these Cu nanoclusters with some explicit examples to explain the atomical-level structure-performance relationship. Finally, the current challenges and future research perspectives with some critical thoughts are elaborated. We hope this review can not only provide a whole picture of the current advances regarding the synthesis and catalytic applications of atomically precise Cu nanoclusters, but also points out some future research visions in this rapidly booming field.

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

Transferable and Interpretable Prediction of Site-Specific Dehydrogenation Reaction Rate Constants with NMR Spectra

Accurate and efficient determination of site-specific reaction rate constants over a wide temperature range remains challenging, both experimentally and theoretically. Taking the dehydrogenation reaction as an example, our study addresses this issue by an innovative combination of machine learning techniques and cost-effective NMR spectra. Through descriptor screening, we identified a minimal set of NMR chemical shifts that can effectively determine reaction rate constants. The constructed model performs exceptionally well on theoretical data sets and demonstrates impressive generalization capabilities, extending from small molecules to larger ones. Furthermore, this model shows outstanding performance when applied to limited experimental data sets, highlighting its robust applicability and transferability. Utilizing the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm, we also present an interpretable rate constant-temperature-NMR (k-T-NMR) relationship with a mathematical formula. This study reveals the great potential of combining machine learning with easily accessible spectroscopic descriptors in the study of reaction kinetics, enabling the rapid determination of reaction rate constants and promoting our understanding of reactivity.

Autonomous mobile robots for exploratory synthetic chemistry

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making1,2. Most autonomous laboratories involve bespoke automated equipment3-6, and reaction outcomes are often assessed using a single, hard-wired characterization technique7. Any decision-making algorithms8 must then operate using this narrow range of characterization data9,10. By contrast, manual experiments tend to draw on a wider range of instruments to characterize reaction products, and decisions are rarely taken based on one measurement alone. Here we show that a synthesis laboratory can be integrated into an autonomous laboratory by using mobile robots11-13 that operate equipment and make decisions in a human-like way. Our modular workflow combines mobile robots, an automated synthesis platform, a liquid chromatography-mass spectrometer and a benchtop nuclear magnetic resonance spectrometer. This allows robots to share existing laboratory equipment with human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal measurement data, selecting successful reactions to take forward and automatically checking the reproducibility of any screening hits. We exemplify this approach in the three areas of structural diversification chemistry, supramolecular host-guest chemistry and photochemical synthesis. This strategy is particularly suited to exploratory chemistry that can yield multiple potential products, as for supramolecular assemblies, where we also extend the method to an autonomous function assay by evaluating host-guest binding properties.

Transforming science labs into automated factories of discovery

Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules and materials to unlock capabilities in energy, catalysis, biotechnology, sustainability, electronics, and more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis and characterization tasks. Robotics and automation can enable scientific experiments to be conducted faster, more safely, more accurately, and with greater reproducibility, allowing scientists to tackle large societal problems in domains such as health and energy on a shorter timescale. We define five levels of laboratory automation, from laboratory assistance to full automation. We also introduce robotics research challenges that arise when increasing levels of automation and when increasing the generality of tasks within the laboratory. Robots are poised to transform science labs into automated factories of discovery that accelerate scientific progress.

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.

In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV-vis Spectroscopy: Implication for Understanding of Heterogeneous Processes

The development of in situ techniques to quantitatively characterize the heterogeneous reactions is essential for understanding physicochemical processes in aqueous phase. In this work, a new approach coupling in situ UV-vis spectroscopy with a two-step algorithm strategy is developed to quantitatively monitor heterogeneous reactions in a compact closed-loop incorporation. The algorithm involves the inverse adding-doubling method for light scattering correction and the multivariate curve resolution-alternating least squares (MCR-ALS) method for spectral deconvolution. Innovatively, theoretical spectral simulations are employed to connect MCR-ALS solutions with chemical molecular structural evolution without prior information for reference spectra. As a model case study, the aqueous adsorption kinetics of bisphenol A onto polyamide microparticles are successfully quantified in a one-step UV-vis spectroscopic measurement. The practical applicability of this approach is confirmed by rapidly screening a superior adsorbent from commercial materials for antibiotic wastewater adsorption treatment. The demonstrated capabilities are expected to extend beyond monitoring adsorption systems to other heterogeneous reactions, significantly advancing UV-vis spectroscopic techniques toward practical integration into automated experimental platforms for probing aqueous chemical processes and beyond.

Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Bottom-Up Film-to-Bulk Assembly Toward Bioinspired Bulk Structural Nanocomposites

Biological materials, although composed of meager minerals and biopolymers, often exhibit amazing mechanical properties far beyond their components due to hierarchically ordered structures. Understanding their structure-properties relationships and replicating them into artificial materials would boost the development of bulk structural nanocomposites. Layered microstructure widely exists in biological materials, serving as the fundamental structure in nanosheet-based nacres and nanofiber-based Bouligand tissues, and implying superior mechanical properties. High-efficient and scalable fabrication of bioinspired bulk structural nanocomposites with precise layered microstructure is therefore important yet remains difficult. Here, one straightforward bottom-up film-to-bulk assembly strategy is focused for fabricating bioinspired layered bulk structural nanocomposites. The bottom-up assembly strategy inherently offers a methodology for precise construction of bioinspired layered microstructure in bulk form, availability for fabrication of bioinspired bulk structural nanocomposites with large sizes and complex shapes, possibility for design of multiscale interfaces, feasibility for manipulation of diverse heterogeneities. Not limited to discussing what has been achieved by using the current bottom-up film-to-bulk assembly strategy, it is also envisioned how to promote such an assembly strategy to better benefit the development of bioinspired bulk structural nanocomposites. Compared to other assembly strategies, the highlighted strategy provides great opportunities for creating bioinspired bulk structural nanocomposites on demand.

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.

Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis

Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.

PolyNC: a natural and chemical language model for the prediction of unified polymer properties

Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories. Here, we present a revolutionary model to enable rapid and precise prediction of Polymer properties via the power of Natural language and Chemical language (PolyNC). To showcase the efficacy of PolyNC, we have meticulously curated a labeled prompt-structure-property corpus encompassing 22 970 polymer data points on a series of essential polymer properties. Through the use of natural language prompts, PolyNC gains a comprehensive understanding of polymer properties, while employing chemical language (SMILES) to describe polymer structures. In a unified text-to-text manner, PolyNC consistently demonstrates exceptional performance on both regression tasks (such as property prediction) and the classification task (polymer classification). Simultaneous and interactive multitask learning enables PolyNC to holistically grasp the structure-property relationships of polymers. Through a combination of experiments and characterizations, the generalization ability of PolyNC has been demonstrated, with attention analysis further indicating that PolyNC effectively learns structural information about polymers from multimodal inputs. This work provides compelling evidence of the potential for deploying end-to-end language models in polymer research, representing a significant advancement in the AI community's dedicated pursuit of advancing polymer science.

Modern Potentiometric Biosensing Based on Non-Equilibrium Measurement Techniques

Modern potentiometric sensors based on polymeric membrane ion-selective electrodes (ISEs) have achieved new breakthroughs in sensitivity, selectivity, and stability and have extended applications in environmental surveillance, medical diagnostics, and industrial analysis. Moreover, nonclassical potentiometry shows promise for many applications and opens up new opportunities for potentiometric biosensing. Here, we aim to provide a concept to summarize advances over the past decade in the development of potentiometric biosensors with polymeric membrane ISEs. This Concept article articulates sensing mechanisms based on non-equilibrium measurement techniques. In particular, we emphasize new trends in potentiometric biosensing based on attractive dynamic approaches. Representative examples are selected to illustrate key applications under zero-current conditions and stimulus-controlled modes. More importantly, fruitful information obtained from non-equilibrium measurements with dynamic responses can be useful for artificial intelligence (AI). The combination of ISEs with advanced AI techniques for effective data processing is also discussed. We hope that this Concept will illustrate the great possibilities offered by non-equilibrium measurement techniques and AI in potentiometric biosensing and encourage further innovations in this exciting field.

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.

Inverse design of chiral functional films by a robotic AI-guided system

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

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.

Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets

The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules.

Machine Learning Descriptors for Data-Driven Catalysis Study

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C-N axial chirality via cobalt catalysis

Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.

Machine learning integrated photocatalysis: progress and challenges

Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.

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.

Machine Learning in Unmanned Systems for Chemical Synthesis

Chemical synthesis is state-of-the-art, and, therefore, it is generally based on chemical intuition or experience of researchers. The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned systems. The ML algorithms and their application scenarios in unmanned systems for chemical synthesis were presented. The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed.

Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters

The surface and interface coordination structures of heterogeneous metal catalysts are crucial to their catalytic performance. However, the complicated surface and interface structures of heterogeneous catalysts make it challenging to identify the molecular-level structure of their active sites and thus precisely control their performance. To address this challenge, atomically dispersed metal catalysts (ADMCs) and ligand-protected atomically precise metal clusters (APMCs) have been emerging as two important classes of model heterogeneous catalysts in recent years, helping to build bridge between homogeneous and heterogeneous catalysis. This review illustrates how the surface and interface coordination chemistry of these two types of model catalysts determines the catalytic performance from multiple dimensions. The section of ADMCs starts with the local coordination structure of metal sites at the metal-support interface, and then focuses on the effects of coordinating atoms, including their basicity and hardness/softness. Studies are also summarized to discuss the cooperativity achieved by dual metal sites and remote effects. In the section of APMCs, the roles of surface ligands and supports in determining the catalytic activity, selectivity, and stability of APMCs are illustrated. Finally, some personal perspectives on the further development of surface coordination and interface chemistry for model heterogeneous metal catalysts are presented.