ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

Heterogeneous catalysis of methane hydroxylation with nearly total selectivity under mild conditions

The efficient utilization of methane, a vital component of natural gas, shale gas and methane hydrate, holds significant importance for global energy security and environmental sustainability. However, converting methane into value-added oxygenates presents a formidable challenge due to its inert nature. Direct selective oxidation of methane (DSOM) under mild conditions is essential for reducing energy consumption and carbon emissions compared with traditional indirect routes. Achieving total selectivity in methane hydroxylation remains elusive due to the competitive CO2 formation. This feature article highlights recent advancements in methane hydroxylation using thermo-, photo-, and electro-catalytic systems. Through strategically designing the structure of catalysts to control the reactive oxygen species and optimizing reaction parameters, significant progress has been made in enhancing oxygenate selectivity and minimizing overoxidation. A comprehensive understanding of the mechanisms underlying methane hydroxylation with total selectivity offers insights for improving catalyst design and reaction parameter optimization, promoting sustainable methane utilization.

When Metal Nanoclusters Meet Smart Synthesis

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis.

The research progress on COF solid-state electrolytes for lithium batteries

Lithium metal batteries have garnered significant attention due to their high energy density and broad application prospects. However, the practical use of traditional liquid electrolytes is constrained by safety and stability challenges. In the exploration of novel electrolytes, solid-state electrolyte materials have emerged as a focal point. Covalent organic frameworks (COFs), with their large conjugated structures and unique electronic properties, are gradually gaining attention as an emerging class of solid-state electrolyte materials. In recent years, outstanding electrochemical performance has been achieved through the design and synthesis of various types of COF-based solid-state electrolytes, along with successful integration with other functional materials. This review will provide an overview of the research progress on COFs as solid-state electrolyte materials for lithium metal batteries and offer insights into their future potential in battery technology.

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.

Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity

In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency (E in %), retained enzymatic activity (A in %), and thermal stability (T in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.

Large Language Models for Inorganic Synthesis Predictions

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.

Physical Chemistry Education and Research in an Open-Sourced Future

Proficiency in physical chemistry requires a broad skill set. Successful trainees often receive mentoring from senior colleagues (research advisors, postdocs, etc.). Mentoring introduces trainees to experimental design, instrumental setup, and complex data interpretation. In lab settings, trainees typically learn by customizing experimental setups, and developing new ways of analyzing data. Learning alongside experts strengthens these fundamentals, and places a focus on the clear communication of research problems. However, this level of input is not scalable, nor can it easily be shared with all researchers or students, particularly those that face socioeconomic barriers to accessing mentoring. New approaches to training will therefore progress the field of physical chemistry. Technology is disrupting and democratising scientific education and research. The emergence of free online courses and video resources enables students to learn in a style that suits them. Higher degrees of automation remove cumbersome and sometimes arbitrary technical barriers to learning new techniques, allowing one to collect high quality data quickly. Open sourcing of data and analysis tools has increased transparency, lowered barriers to access, and accelerated scientific dissemination. However, these advances also can lead to "black box" approaches to acquiring and analyzing data, where convenience replaces understanding and errors and misrepresentations become more common. The risk is a breakdown in education: if one does not understand the fundamentals of a technique or analysis, it is difficult to correctly discern the practical limits of an experiment, distinguish signal from noise, troubleshoot problems, or take full advantage of powerful analytical procedures. Our vision of the future of physical chemistry is built around democratized learning, where deep technical and analytical expertise from physical chemists is made freely available. Advancements in technical education through expert-generated educational resources and AI-based tools will enrich physical chemistry education. A holistic approach to education will prepare the physical chemists of 2050 to adapt to rapidly advancing technological tools, which accelerate the pace of research. Technical education will be enhanced by accessible open-source instrumentation and analysis procedures, which will provide instruments and analysis scripts specifically designed for education. High quality, comparable data from standardized open-source instruments will feed into accessible databases and analysis projects, providing others the opportunity to store and analyze both failed and successful experiments. The coupling of open-source education, hardware, and analysis will democratize physical chemistry while addressing risks associated with "black box" approaches.

Isoreticular Curves: A Theory of Capillary Condensation To Model Water Sorption within Microporous Sorbents

Metal-organic frameworks have gained traction as leading materials for water sorption applications due to precise chemical tunability of their well-ordered pores. These applications include atmospheric water capture, heat pumps, desiccation, desalination, humidity control, and thermal batteries. However, the relationships between the framework pore structure and the measurable water sorption properties, namely critical relative humidity for condensation, maximal capacity, and pore size or temperature for the onset of hysteresis, have not been clearly delineated. Herein, we precisely formulate these relationships by application of the theory of capillary condensation and macroscopic thermodynamic models to a large data set of MOF water isotherms. These relationships include a concept termed isoreticular curves that relates the critical pressure for pore condensation (α), gravimetric capacity (Qmax), and hydrophilicity (the Gibbs free energy for binding water, ΔG) as Qmax = a1(ΔG/ln α)2 + a2(ΔG/ln α), with constants a1 and a2 dependent upon the density and volume occupied by the linker and secondary building unit, and framework topology. Through this analysis, we propose guidelines for the maximization of sorption capacity at a given relative humidity with minimal hysteresis and discuss the theoretical limits for capacity at low relative humidity. This model provides an explanation for the lack of high-capacity frameworks at low relative humidity, as increasing pore size also causes an increase in relative humidity. We propose a loose upper bound of Qmax = -0.25(1/ln α)2 - 1.75(1/ln α) for the limit of maximal capacity at a given relative humidity in the dry regime. These guidelines are consequential for the design of new materials for water sorption applications.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

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.

Synthetic Strategies to Extended Aromatic Covalent Organic Frameworks

π-Conjugated materials are highly attractive owing to their unique optical and electronic properties. Covalent organic frameworks (COFs) offer a great opportunity for precise arrangement of building units in a π-conjugated crystalline matrix and tuning of the properties through choice of functionalities or post-synthetic modification. With this review, we aim at summarizing both the most representative as well as emerging strategies for the synthesis of π-conjugated COFs. We give examples of direct synthesis using large, π-extended building blocks. COFs featuring fully conjugated linkages such as vinylene, pyrazine, and azole are discussed. Then, post-synthetic modification methods that result in the extension of the COF π-system are reviewed. Throughout, mechanistic insights are presented when available. In the context of their utilization as film devices, we conduct a concise survey of the prominent COF layer deposition techniques reported and their aptness for the deposition of fused aromatic systems.

Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy

This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NOx with NH3 as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions.

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.