Crystalline Polyphenylene Covalent Organic Frameworks.

The synthesis of crystalline polyphenylene covalent organic frameworks (COFs) was accomplished by linking fluorinated tris(4-acetylphenyl)benzene building units using aldol cyclotrimerization. The structures of the two COFs, reported here, were confirmed by powder X-ray diffraction techniques, Fourier transform infrared, and solid-state 13C CP/MAS NMR spectroscopy. The results showed that the COFs were porous and chemically stable in corrosive, harsh environments for at least 1 week. Accordingly, postsynthetically modified derivatives of these COFs using primary amines showed CO2 uptake from air and flue gas.

Harvesting Water from Air with High-Capacity, Stable Furan-Based Metal-Organic Frameworks.

We synthesized two isoreticular furan-based metal-organic frameworks (MOFs), MOF-LA2-1(furan) and MOF-LA2-2(furan) with rod-like secondary building units (SBUs) featuring 1D channels, as sorbents for atmospheric water harvesting (LA = long arm). These aluminum-based MOFs demonstrated a combination of high water uptake and stability, exhibiting working capacities of 0.41 and 0.48 gwater/gMOF (under isobaric conditions of 1.70 kPa), respectively. Remarkably, both MOFs showed a negligible loss in water uptake after 165 adsorption-desorption cycles. These working capacities rival that of MOF-LA2-1(pyrazole), which has a working capacity of 0.55 gwater/gMOF. The current MOFs stand out for their high water stability, as evidenced by 165 cycles of water uptake and release. MOF-LA2-2(furan) is the first aluminum MOF to employ a double 'long arm' extension strategy, which is confirmed through single-crystal X-ray diffraction (SCXRD). The MOFs were synthesized by using a straightforward synthesis route. This study offers valuable insights into the design of durable, water-stable MOFs and underscores their potential for efficient water harvesting.

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.

Shaping the Water-Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models.

We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g-1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.

Structural Chemistry of Zeolitic Imidazolate Frameworks.

Zeolitic imidazolate frameworks (ZIFs) are a subclass of reticular structures based on tetrahedral four-connected networks of zeolites and minerals. They are composed of transition-metal ions and imidazolate-type linkers, and their pore size and shape, surface area, and functionality can be precisely controlled. Despite their potential, two questions remain unanswered: how to synthesize more diverse ZIF structures and how ZIFs differentiate from other crystalline solids. In other words, how can we use our understanding of their unique structures to better design and synthesize ZIFs? In this Review, we first summarize the methods for synthesizing a wide range of ZIFs. We then review the crystal structure of ZIFs and describe the relationship between their structure and properties using an in-depth analysis. We also discuss several important and intrinsic features that make ZIFs stand out from MOFs and discrete molecular cages. Finally, we outline the future direction for this class of porous crystals.

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.

Broadly Tunable Atmospheric Water Harvesting in Multivariate Metal-Organic Frameworks.

Development of multivariate metal-organic frameworks (MOFs) as derivatives of the state-of-art water-harvesting material MOF-303 {[Al(OH)(PZDC)], where PZDC2- is 1H-pyrazole-3,5-dicarboxylate} was shown to be a powerful tool to generate efficient water sorbents tailored to a given environmental condition. Herein, a new multivariate MOF-303-based water-harvesting framework series from readily available reactants is developed. The resulting MOFs exhibit a larger degree of tunability in the operational relative humidity range (16%), regeneration temperature (14 °C), and desorption enthalpy (5 kJ mol-1) than reported previously. Additionally, a high-yielding (≥90%) and scalable (∼3.5 kg) synthesis is demonstrated in water and with excellent space-time yields, without compromising framework crystallinity, porosity, and water-harvesting performance.

MOF Linker Extension Strategy for Enhanced Atmospheric Water Harvesting.

A linker extension strategy for generating metal-organic frameworks (MOFs) with superior moisture-capturing properties is presented. Applying this design approach involving experiment and computation results in MOF-LA2-1 {[Al(OH)(PZVDC)], where PZVDC2- is (E)-5-(2-carboxylatovinyl)-1H-pyrazole-3-carboxylate}, which exhibits an approximately 50% water capacity increase compared to the state-of-the-art water-harvesting material MOF-303. The power of this approach is the increase in pore volume while retaining the ability of the MOF to harvest water in arid environments under long-term uptake and release cycling, as well as affording a reduction in regeneration heat and temperature. Density functional theory calculations and Monte Carlo simulations give detailed insight pertaining to framework structure, water interactions within its pores, and the resulting water sorption isotherm.

High-yield, green and scalable methods for producing MOF-303 for water harvesting from desert air.

Metal-organic frameworks (MOFs) are excellent candidates for water harvesting from desert air. MOF-303 (Al(OH)(PZDC), where PZDC is 1-H-pyrazole-3,5-dicarboxylate), a robust and water-stable MOF, is a particularly promising water-harvesting sorbent that can take up water at low relative humidity and release it under mild heating. Accordingly, development of a facile, high-yield synthesis method for its production at scale is highly desirable. Here we report detailed protocols for the green, water-based preparation of MOF-303 on both gram and kilogram scales. Specifically, four synthetic methods (solvothermal, reflux, vessel and microwave), involving different equipment requirements, are presented to guarantee general accessibility. Typically, the solvothermal method takes ~24 h to synthesize MOF-303, while the reflux and vessel methods can reduce the time to 4-8 h. With the microwave-assisted method, the reaction time can be further reduced to just 5 min. In addition, we provide guidance on the characterization of MOF-303, as well as water-harvesting MOFs in general, to ensure high quality of the product in terms of its purity, crystallinity, porosity and water uptake. Furthermore, to address the need for future commercialization of this material, we demonstrate that our protocol can be employed to produce 3.5 kg per batch with a yield of 91%. MOF-303 synthesized at this large scale shows similar crystallinity and water uptake capacity compared to the respective material produced at a small scale. Our synthetic procedure is green and water-based, and can produce the MOF within hours.

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.

Gold-catalyzed hydroalkoxylation of alkoxyallenes.

We report synthesis of allylic acetals via gold-catalyzed hydroalkoxylation of alkoxyallenes with alcohols containing primary and secondary alcohols in good to excellent yields under mild condition.