Automated Chemical Reaction Extraction from Scientific Literature

Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic Compounds

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from -170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

Machine learning-guided strategies for reaction conditions design and optimization

This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry.

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.

MPNTEXT: An Interactive Platform for Automatically Extracting Metal-Polyphenol Networks and Their Applications from Scientific Literature

In recent years, metal-polyphenol networks (MPNs) have gained significant attention due to their unique properties and broad applications across various fields. However, the burgeoning volume of MPN literature necessitates the automation of chemical information extraction from the extensive corpus of unstructured data, including scientific publications. To address this challenge, we proposed a platform named MPNTEXT, which utilized natural language processing techniques and machine learning algorithms to efficiently identify and extract pertinent information, thereby assisting users in comprehending complex MPNs and their textual descriptions of applications. Users can enter keywords, such as "Fe", "drug delivery", or "tannic acid", to retrieve relevant information, which is then presented in a structured format. This study aims to provide a user-friendly tool for collecting and retrieving MPN data and promotes data-driven material design. The platform offers researchers a more convenient and efficient way to design versatile MPNs and explore their applications.

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.

The Block Copolymer Phase Behavior Database

The Block Copolymer Database (BCDB) is a platform that allows users to search, submit, visualize, benchmark, and download experimental phase measurements and their associated characterization information for di- and multiblock copolymers. To the best of our knowledge, there is no widely accepted data model for publishing experimental and simulation data on block copolymer self-assembly. This proposed data schema with traceable information can accommodate any number of blocks and at the time of publication contains over 5400 block copolymer total melt phase measurements mined from the literature and manually curated and simulation data points of the phase diagram generated from self-consistent field theory that can rapidly be augmented. This database can be accessed via the Community Resource for Innovation in Polymer Technology (CRIPT) web application and the Materials Data Facility. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the SMARTS search syntax (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using Structured Query Language (SQL) and download custom sets of block copolymer data to train machine learning models. Finally, a protocol is presented in which GPT-4, an AI-powered large language model, can be used to rapidly screen and identify block copolymer papers from the literature using only the abstract text and determine whether they have BCDB data, allowing the database to grow as the number of published papers on the World Wide Web increases. The F1 score for this model is 0.74. This platform is an important step in making polymer data more accessible to the broader community.

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.

OpenChemIE: An Information Extraction Toolkit for Chemistry Literature

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of OpenChemIE attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. OpenChemIE is most suited for information extraction on organic chemistry literature, where molecules are generally depicted as planar graphs or written in text and can be consolidated into a SMILES format. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.

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 US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

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.

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing

In recent times, there has been a substantial increase in the number of articles focusing on antioxidants. However, the development of a comprehensive estimator for antioxidant capacity remains elusive due to the challenge of integrating information from these articles. Furthermore, the complexity of the antioxidant mechanism, which involves a multitude of factors, makes it difficult to establish a simple equation or correlation. Hence, there is a pressing need for a model that can effectively interpret the collective knowledge from these articles, especially from a chemistry perspective. In this research, we employed natural language processing techniques, specifically Word2Vec, to analyze articles related to antioxidant capacity. We extracted representation vectors of compound names from these documents and organized them into 10 distinct clusters. In our investigation of two of these clusters, we unveiled that the majority of the compounds in question were flavonoids and flavonoid glycosides. To establish a link between the descriptors and clusters, we utilized kernel density estimation and generated scatter plots to visualize their similarity. These visualizations clearly indicated a strong relationship between the descriptors and clusters, affirming that a tangible connection exists between word vectors and compound descriptors through a document analysis conducted with natural language processing techniques. This study represents a pioneering approach that utilizes document analysis to shed light on the field of antioxidant capacity research, marking a significant advancement in this domain.

Attention is all you need: utilizing attention in AI-enabled drug discovery

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

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.

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature

The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews

BACKGROUND

Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.

METHODS

We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model. Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type.

RESULTS

For entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types.

CONCLUSIONS

The performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Mechanism to model: a physical organic chemistry approach to reaction prediction

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis.

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing

Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe.

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature. We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is made available at polymerscholar.org which can be used to locate material property data recorded in abstracts. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with extracted material property information.

MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation

Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and bonds, along with their geometric layouts, to construct the molecular structure. Our model flexibly incorporates symbolic chemistry constraints to recognize chirality and expand abbreviated structures. We further develop data augmentation strategies to enhance the model robustness against domain shifts. In experiments on both synthetic and realistic molecular images, MolScribe significantly outperforms previous models, achieving 76-93% accuracy on public benchmarks. Chemists can also easily verify MolScribe's prediction, informed by its confidence estimation and atom-level alignment with the input image. MolScribe is publicly available through Python and web interfaces: https://github.com/thomas0809/MolScribe.

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.

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models' performance.

Cascade Processes with Micellar Reaction Media: Recent Advances and Future Directions

Reducing the use of solvents is an important aim of green chemistry. Using micelles self-assembled from amphiphilic molecules dispersed in water (considered a green solvent) has facilitated reactions of organic compounds. When performing reactions in micelles, the hydrophobic effect can considerably accelerate apparent reaction rates, as well as enhance selectivity. Here, we review micellar reaction media and their potential role in sustainable chemical production. The focus of this review is applications of engineered amphiphilic systems for reactions (surface-active ionic liquids, designer surfactants, and block copolymers) as reaction media. Micelles are a versatile platform for performing a large array of organic chemistries using water as the bulk solvent. Building on this foundation, synthetic sequences combining several reaction steps in one pot have been developed. Telescoping multiple reactions can reduce solvent waste by limiting the volume of solvents, as well as eliminating purification processes. Thus, in particular, we review recent advances in “one-pot” multistep reactions achieved using micellar reaction media with potential applications in medicinal chemistry and agrochemistry. Photocatalyzed reactions in micellar reaction media are also discussed. In addition to the use of micelles, we emphasize the process (steps to isolate the product and reuse the catalyst).

Prediction of solar cell materials via unsupervised literature learning

Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the natural language processing (NLP) technique to unsupervisedly predict the existence of solar cell types including the dye-sensitized solar cells and the perovskite solar cells based on literatures published prior to their first discovery without human annotation. Enlightened by this, we further identify possible solar cell material candidates via NLP starting with a comprehensive training database of 3.2 million paper abstracts published before 2021. The NLP model effectively predicts the existing solar cell materials, while an uncommon solar cell material namely PtSe₂is suggested as an appropriate candidate for the future solar cells. Its optoelectronic properties are comprehensive investigated via first-principles calculations to reveal the decent stability and optoelectronic performance of the NLP-predicted candidate. This study demonstrates the viability of the textual data for the data-driven materials prediction and highlights the NLP method as a powerful tool to reliably predict the solar cell materials.