MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

Evaluation of Open-Source Large Language Models for Metal-Organic Frameworks Research

Along with the development of machine learning, deep learning, and large language models (LLMs) such as GPT-4 (GPT: Generative Pre-Trained Transformer), artificial intelligence (AI) tools have been playing an increasingly important role in chemical and material research to facilitate the material screening and design. Despite the exciting progress of GPT-4 based AI research assistance, open-source LLMs have not gained much attention from the scientific community. This work primarily focused on metal-organic frameworks (MOFs) as a subdomain of chemistry and evaluated six top-rated open-source LLMs with a comprehensive set of tasks including MOFs knowledge, basic chemistry knowledge, in-depth chemistry knowledge, knowledge extraction, database reading, predicting material property, experiment design, computational scripts generation, guiding experiment, data analysis, and paper polishing, which covers the basic units of MOFs research. In general, these LLMs were capable of most of the tasks. Especially, Llama2-7B and ChatGLM2-6B were found to perform particularly well with moderate computational resources. Additionally, the performance of different parameter versions of the same model was compared, which revealed the superior performance of higher parameter versions.

Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology

Metal-organic frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, zeolitic imidazolate framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view for the behavior of the system. To provide a unified view, we have generated a comprehensive data set of synthetic conditions and their final outputs and applied machine learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given sub-space of synthetic variables space (chemical space) and to reveal their impact on important features such as final particle size and morphology. By doing so, we draw connections and establish a hierarchy for the role of each synthetic variable and provide with rule of thumb for attaining control on the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream reported trends, which can guide the rational design of ZIF-8 particles which ultimately determine their suitability for any given targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind the synthesis procedures employed for their synthesis.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design

Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.

Utilizing Data Mining for the Synthesis of Functionalized Tungsten Oxide with Enhanced Oxygen Vacancies for Highly Sensitive Detection of Triethylamine

The optimal combination of metal ions and ligands for sensing materials was estimated by using a data-driven model developed in this research. This model utilized advanced computational algorithms and a data set of 100,000 literature pieces. The semiconductor metal oxide (SMO) that is most suitable for detecting triethylamine (TEA) with the highest probability was identified by using the Word2vec model, which employed the maximum likelihood method. The loss function of the probability distribution was minimized in this process. Based on the analysis, a novel hierarchical nanostructured tungsten-based coordination with 2,5-dihydroxyterephthalic acid (W-DHTA) was synthesized. This synthesis involved a postsynthetic hydrothermal treatment (psHT) and the self-assembly of tungsten oxide nanorods. The tungsten oxide nanorods had a significant number of oxygen vacancies. Various techniques were used to characterize the synthesized material, and its sensing performance toward volatile organic compound (VOC) gases was evaluated. The results showed that the functionalized tungsten oxide exhibited an exceptionally high sensitivity and selectivity toward TEA gas. Even in a highly disturbed environment, the detection limit for TEA gas was as low as 40 parts per billion (ppb). Furthermore, our findings suggest that the control of oxygen vacancies in sensing materials plays a crucial role in enhancing the sensitivity and selectivity of gas sensors. This approach was supported by the utilization of density functional theory (DFT) computation and machine learning algorithms to assess and analyze the performance of sensor devices, providing a highly efficient and universally applicable research methodology for the development and design of next-generation functional materials.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal-organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.

Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.

Fabrication of Oriented Polycrystalline MOF Superstructures

The field of metal-organic frameworks (MOFs) has progressed beyond the design and exploration of powdery and single-crystalline materials. A current challenge is the fabrication of organized superstructures that can harness the directional properties of the individual constituent MOF crystals. To date, the progress in the fabrication methods of polycrystalline MOF superstructures has led to close-packed structures with defined crystalline orientation. By controlling the crystalline orientation, the MOF pore channels of the constituent crystals can be aligned along specific directions: these systems possess anisotropic properties including enhanced diffusion along specific directions, preferential orientation of guest species, and protection of functional guests. In this perspective, we discuss the current status of MOF research in the fabrication of oriented polycrystalline superstructures focusing on the specific crystalline directions of orientation. Three methods are examined in detail: the assembly from colloidal MOF solutions, the use of external fields for the alignment of MOF particles, and the heteroepitaxial ceramic-to-MOF growth. This perspective aims at promoting the progress of this field of research and inspiring the development of new protocols for the preparation of MOF systems with oriented pore channels, to enable advanced MOF-based devices with anisotropic properties.

High-aspect-ratio nanostructured hydroxyapatite: towards new functionalities for a classical material

Hydroxyapatite-based materials have been widely used in countless applications, such as bone regeneration, catalysis, air and water purification or protein separation. Recently, much interest has been given to controlling the aspect ratio of hydroxyapatite crystals from bulk samples. The ability to exert control over the aspect ratio may revolutionize the applications of these materials towards new functional materials. Controlling the shape, size and orientation of HA crystals allows obtaining high aspect ratio structures, improving several key properties of HA materials such as molecule adsorption, ion exchange, catalytic reactions, and even overcoming the well-known brittleness of ceramic materials. Regulating the morphogenesis of HA crystals to form elongated oriented fibres has led to flexible inorganic synthetic sponges, aerogels, membranes, papers, among others, with applications in sustainability, energy and catalysis, and especially in the biomedical field.

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.

Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid

Copper-based nanocrystals are reference nanomaterials for integration into emerging green technologies, with laser ablation in liquid (LAL) being a remarkable technique for their synthesis. However, the achievement of a specific type of nanocrystal, among the whole library of nanomaterials available using LAL, has been until now an empirical endeavor based on changing synthesis parameters and characterizing the products. Here, we started from the bibliographic analysis of LAL synthesis of Cu-based nanocrystals to identify the relevant physical and chemical features for the predetermination of copper oxidation state. First, single features and their combinations were screened by linear regression analysis, also using a genetic algorithm, to find the best correlation with experimental output and identify the equation giving the best prediction of the LAL results. Then, machine learning (ML) models were exploited to unravel cross-correlations between features that are hidden in the linear regression analysis. Although the LAL-generated Cu nanocrystals may be present in a range of oxidation states, from metallic copper to cuprous oxide (Cu2O) and cupric oxide (CuO), in addition to the formation of other materials such as Cu2S and CuCN, ML was able to guide the experiments toward the maximization of the compounds in the greatest demand for integration in sustainable processes. This approach is of general applicability to other nanomaterials and can help understand the origin of the chemical pathways of nanocrystals generated by LAL, providing a rational guideline for the conscious predetermination of laser-synthesis parameters toward the desired compounds.

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

A GPT-4 Reticular Chemist for Guiding MOF Discovery

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.

Application of Fe-MOFs in Photodegradation and Removal of Air and Water Pollutants: A Review

Photocatalytic technology has received increasing attention in recent years. A pivotal facet of photocatalytic technology lies in the development of photocatalysts. Porous metal-organic framework (MOF) materials, distinguished by their unique properties and structural characteristics, have emerged as a focal point of research in the field, finding widespread application in the photo-treatment and conversion of various substances. Fe-based MOFs have attained particular prominence. This review explores recent advances in the photocatalytic degradation of aqueous and gaseous substances. Furthermore, it delves into the interaction between the active sites of Fe-MOFs and pollutants, offering deeper insights into their mechanism of action. Fe-MOFs, as photocatalysts, predominantly facilitate pollutant removal through redox processes, interaction with acid sites, the formation of complexes with composite metal elements, binding to unsaturated metal ligands (CUSs), and hydrogen bonding to modulate their respiratory behavior. This review also highlights the focal points of future research, elucidating the challenges and opportunities that lie ahead in harnessing the characteristics and advantages of Fe-MOF composite catalysts. In essence, this review provides a comprehensive summary of research progress on Fe-MOF-based catalysts, aiming to serve as a guiding reference for other catalytic processes.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

Beyond Solvothermal: Alternative Synthetic Methods for Covalent Organic Frameworks

Covalent organic frameworks (COFs) are crystalline porous organic materials that hold a wealth of potential applications across various fields. The development of COFs, however, is significantly impeded by the dearth of efficient synthetic methods. The traditional solvothermal approach, while prevalent, is fraught with challenges such as complicated processes, excessive energy consumption, long reaction times, and limited scalability, rendering it unsuitable for practical applications. The quest for simpler, quicker, more energy-efficient, and environmentally benign synthetic strategies is thus paramount for bridging the gap between academic COF chemistry and industrial application. This Review provides an overview of the recent advances in alternative COF synthetic methods, with a particular emphasis on energy input. We discuss representative examples of COF synthesis facilitated by microwave, ultrasound, mechanic force, light, plasma, electric field, and electron beam. Perspectives on the advantages and limitations of these methods against the traditional solvothermal approach are highlighted.

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

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.

Quid Pro Flow

How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10^-9 m² s^-1 for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics.

Saiz P, Reizabal A, Wuttke S, Celaya-Azcoaga L, Valverde A, Fernández de Luis R. A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation

Hexavalent chromium (Cr(VI)) is a highly mobile cancerogenic and teratogenic heavy metal ion. Among the varied technologies applied today to address chromium water pollution, photocatalysis offers a rapid reduction of Cr(VI) to the less toxic Cr(III). In contrast to classic photocatalysts, Metal-Organic frameworks (MOFs) are porous semiconductors that can couple the Cr(VI) to Cr(III) photoreduction to the chromium species immobilization. In this minireview, we wish to discuss and analyze the state-of-the-art of MOFs for Cr(VI) detoxification and contextualizing it to the most recent advances and strategies of MOFs for photocatalysis purposes. The minireview has been structured in three sections: (i) a detailed discussion of the specific experimental techniques employed to characterize MOF photocatalysts, (ii) a description and identification of the key characteristics of MOFs for Cr(VI) photoreduction, and (iii) an outlook and perspective section in order to identify future trends.

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Metalated covalent organic frameworks: from synthetic strategies to diverse applications

Covalent organic frameworks (COFs) are a class of organic crystalline porous materials discovered in the early 21st century that have become an attractive class of emerging materials due to their high crystallinity, intrinsic porosity, structural regularity, diverse functionality, design flexibility, and outstanding stability. However, many chemical and physical properties strongly depend on the presence of metal ions in materials for advanced applications, but metal-free COFs do not have these properties and are therefore excluded from such applications. Metalated COFs formed by combining COFs with metal ions, while retaining the advantages of COFs, have additional intriguing properties and applications, and have attracted considerable attention over the past decade. This review presents all aspects of metalated COFs, from synthetic strategies to various applications, in the hope of promoting the continued development of this young field.

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof‐synthesis.aimat.science.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.