A data-driven perspective on the colours of metal-organic frameworks

How Reproducible is the Synthesis of Zr-Porphyrin Metal-Organic Frameworks? An Interlaboratory Study

Metal-organic frameworks (MOFs) are a rapidly growing class of materials that offer great promise in various applications. However, the synthesis remains challenging: for example, a range of crystal structures can often be accessed from the same building blocks, which complicates the phase selectivity. Likewise, the high sensitivity to slight changes in synthesis conditions may cause reproducibility issues. This is crucial, as it hampers the research and commercialization of affected MOFs. Here, it presents the first-ever interlaboratory study of the synthetic reproducibility of two Zr-porphyrin MOFs, PCN-222 and PCN-224, to investigate the scope of this problem. For PCN-222, only one sample out of ten was phase pure and of the correct symmetry, while for PCN-224, three are phase pure, although none of these show the spatial linker order characteristic of PCN-224. Instead, these samples resemble dPCN-224 (disordered PCN-224), which has recently been reported. The variability in thermal behavior, defect content, and surface area of the synthesised samples are also studied. The results have important ramifications for field of metal-organic frameworks and their crystallization, by highlighting the synthetic challenges associated with a multi-variable synthesis space and flat energy landscapes characteristic of MOFs.

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.

Cheminformatics Microservice: unifying access to open cheminformatics toolkits

In recent years, cheminformatics has experienced significant advancements through the development of new open-source software tools based on various cheminformatics programming toolkits. However, adopting these toolkits presents challenges, including proper installation, setup, deployment, and compatibility management. In this work, we present the Cheminformatics Microservice. This open-source solution provides a unified interface for accessing commonly used functionalities of multiple cheminformatics toolkits, namely RDKit, Chemistry Development Kit (CDK), and Open Babel. In addition, more advanced functionalities like structure generation and Optical Chemical Structure Recognition (OCSR) are made available through the Cheminformatics Microservice based on pre-existing tools. The software service also enables developers to extend the functionalities easily and to seamlessly integrate them with existing workflows and applications. It is built on FastAPI and containerized using Docker, making it highly scalable. An instance of the microservice is publicly available at https://api.naturalproducts.net . The source code is publicly accessible on GitHub, accompanied by comprehensive documentation, version control, and continuous integration and deployment workflows. All resources can be found at the following link: https://github.com/Steinbeck-Lab/cheminformatics-microservice .

Accurate, interpretable predictions of materials properties within transformer language models

Property prediction accuracy has long been a key parameter of machine learning in materials informatics. Accordingly, advanced models showing state-of-the-art performance turn into highly parameterized black boxes missing interpretability. Here, we present an elegant way to make their reasoning transparent. Human-readable text-based descriptions automatically generated within a suite of open-source tools are proposed as materials representation. Transformer language models pretrained on 2 million peer-reviewed articles take as input well-known terms such as chemical composition, crystal symmetry, and site geometry. Our approach outperforms crystal graph networks by classifying four out of five analyzed properties if one considers all available reference data. Moreover, fine-tuned text-based models show high accuracy in the ultra-small data limit. Explanations of their internal machinery are produced using local interpretability techniques and are faithful and consistent with domain expert rationales. This language-centric framework makes accurate property predictions accessible to people without artificial-intelligence expertise.

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.

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al₂(OH)₂TCPP) [H₂TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.

Exploring electronic lab notebooks (ELNs) at a R1 institution in the Southeast USA

Purpose

This study aims to build a better understanding of researcher needs regarding support for data that you create, store, and/or manage using an electronic lab notebook (ELN), also referred to as electronic research notebook (ERN). The study also articulates the need for risk assessment for ELN products used by researchers for both open data and sensitive data that require standards.

Design/methodology/approach

The author used a participatory action research mixed-methods approach. A working group was formed from an ELN initial meeting. The working group team investigated several institutional ERN solutions by setting up trials, speaking with representatives from other research universities with ERN solutions and conducting internal and external research. This culminated in a broader-scale survey exploration.

Findings

Findings reveal there is no single institutional ELN license solution to satisfy all scientific disciplines. There is a need to develop foundational tools needed by all, provide additional tools and uses cases with best practices that can be tailored to various labs and research processes and develop a how-to guide on how to assemble the parts to create a useful ELN solution.

Research limitations/implications

The research implications include providing support for researchers selecting an ERN solution through a combination of online guides, short tutorials and training. There is a need to develop foundational tools, uses cases with best practices that can be tailored to various labs and research processes and how-to guide on how to assemble the parts to create a useful hybrid-ELN solution.

Practical implications

Practical implications include aligning available ERN solutions with other institution provided technologies across the research life cycle to provide researchers a suite of tools to conduct and manage their research. Further investigating educational license discounts for courses using eLabJournal, RSpace, Protocols.io, Open Science Framework, LabArchives or other ERNs currently funded by student course fees via grant funded projects are key implications.

Social implications

Social implications include the research computing environments of researchers that use ELN solutions approved through institutional risk assessment for open data are in compliance with university regulatory frameworks for use of the software in research.

Originality/value

The originality of this study includes risk assessments of ELNs solutions to better guide researchers in the selection process. To the best of the author’s knowledge, this survey was the first exploration of ELN on campus resulting in a final report to senior stakeholders. This study also highlights a developing grant proposal to further develop support across labs and campus.

Making the collective knowledge of chemistry open and machine actionable

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.