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Computational Modeling Helps Design “Ultrastable” Metal-Organic Frameworks for Various Applications

Ultra-durable Materials

Metal-organic frameworks (MOFs) are a class of materials that possess an ordered and porous structure, making them attractive for various applications such as gas storage, separation, and catalysis. However, not all MOFs have the stability required for practical use, and predicting which structures will be the most stable is a challenge. To address this problem, a team of researchers from the Massachusetts Institute of Technology (MIT) has developed a computational approach to identify “ultrastable” MOF structures that are promising candidates for diverse applications, including greenhouse gas capture and conversion.

The MIT team led by Associate Professor of Chemistry and Chemical Engineering Heather Kulik developed a machine-learning model that allowed them to predict MOF structures with high stability. The researchers used their model to screen over 700,000 hypothetical MOF structures and identified approximately 10,000 ultrastable MOF structures that have not been previously reported. These structures have a diverse range of chemical compositions and pore sizes, making them attractive for various applications.

The computational approach used by the MIT team involves predicting the energy required to form a MOF structure and assessing its structural stability. The researchers used a machine-learning algorithm called the random forest regression model to analyze a database of known MOF structures and their stability data. They then used this information to design hypothetical building blocks that were expected to be highly stable.

The researchers combined these building blocks to create new MOF structures that had not been previously reported. They then assessed the stability of these structures using density functional theory (DFT) calculations. The team found that their ultrastable MOFs had high stability even under harsh conditions such as high temperature and pressure.

One potential application for ultrastable MOFs is the conversion of methane gas to methanol. Methane is a potent greenhouse gas, and converting it to methanol would provide a useful fuel while reducing greenhouse gas emissions. The MIT team identified several MOFs that could potentially catalyze this conversion reaction with high efficiency and selectivity.

The computational approach used by the MIT team has several advantages over traditional experimental methods for designing MOFs. The approach is faster and more efficient, and it allows researchers to screen a vast number of possible MOF structures. Additionally, the approach can help researchers identify novel MOFs that have not been previously reported, which could lead to the discovery of new materials with useful properties.

The MIT team’s research has several implications for the field of materials science. By combining machine learning and DFT calculations, researchers can design and predict the stability of new materials with a high degree of accuracy. This approach could be applied to other classes of materials beyond MOFs, leading to the discovery of new materials with unique properties and applications.

In conclusion, the MIT team’s computational approach to design ultrastable MOFs has the potential to accelerate the development of new materials for various applications. The approach combines machine learning and DFT calculations to predict the stability of MOF structures, allowing researchers to identify promising candidates for diverse applications. The approach could be applied to other classes of materials, leading to the discovery of new materials with unique properties and applications.

Computational Modeling for Designing Ultrastable Metal-Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of porous materials that have shown great potential for various applications such as gas storage, catalysis, drug delivery, and sensing. They are composed of organic linkers and metal nodes, which assemble in a crystalline lattice structure. Due to the vast number of possible combinations of linkers and metal nodes, MOFs exhibit an exceptional structural diversity, making them ideal for a wide range of applications. However, not all MOF structures are stable enough to be used in practical applications. To overcome this challenge, scientists at MIT have developed a computational approach to predict the stability of MOFs for various applications.

The research, led by MIT associate professor of chemistry and chemical engineering, Heather Kulik, involved designing stable MOFs using computational modeling. By using their computational model, they identified around 10,000 possible MOF structures that they classify as “ultrastable” for applications such as converting methane gas to methanol. This approach is much faster than the traditional trial-and-error approach, which involves synthesizing and testing many different MOF structures. The study also introduces a new database of ultrastable MOFs that can be used as a starting point for the design of new materials.

MOFs are made up of metal ions, such as zinc or copper, that are coordinated with organic ligands. These materials have a unique, cage-like structure, and their porosity can be adjusted by selecting the metal ion and organic ligand combinations. Due to their porous structure, MOFs can be used for the storage of gases, such as hydrogen, methane, and carbon dioxide, and have potential applications in gas separation and purification. MOFs have also been studied for their potential use in catalysis, sensing, drug delivery, and imaging.

One of the major challenges in MOF research is to design MOFs that are stable and can withstand harsh conditions over time. Traditional methods of MOF design have relied on trial-and-error experimentation, which can be time-consuming and costly. To address this issue, researchers have developed computational models that can predict the stability of MOFs based on their structures. These models can reduce the time and cost of designing new MOFs and increase the likelihood of success in synthesizing stable MOFs.

In the recent study, Kulik and her team developed a machine-learning model that can predict the stability of MOFs based on their structural features. The model uses data from a few thousand papers on MOFs to predict the temperature at which a given MOF would break down and whether particular MOFs can withstand the conditions needed to remove solvents used to synthesize them. The computer model was trained to predict these two features, known as thermal stability and activation stability, based on the molecules’ structure.

Using this model, the researchers identified around 500 MOFs with very high stability. They then broke these MOFs down into their most common building blocks, 120 secondary building units, and 16 linkers. By recombining these building blocks using about 750 different types of architectures, the researchers generated around 50,000 new MOF structures. This approach allowed them to create a database of MOFs that are highly stable and could be useful for various applications.

One of the unique features of the study is that the researchers looked at a more diverse range of crystal symmetries than had ever been looked at before. They also used building blocks that had only come from experimentally synthesized highly stable MOFs. By combining these two aspects, they were able to generate a large number of new MOF structures with high stability.

Implications and Funding Sources

Researchers at MIT have used machine learning to develop a database of over 10,000 ultrastable metal organic frameworks (MOFs) for gas storage and separation. MOFs are materials that consist of organic molecules called linkers that connect secondary building units of metal atoms. They have a porous structure, which makes them ideal for gas storage, separation, and conversion. However, designing MOFs with the desired stability and properties is a challenging task that usually involves trial and error.

The researchers, led by Heather J. Kulik, an associate professor of chemical engineering at MIT, have developed a new computational model to predict the stability of MOFs based on their structure. The model was trained on a dataset of over 2,500 papers on MOFs to predict the thermal and activation stability of MOFs based on their molecules’ structure. The researchers used the model to identify over 500 MOFs with high stability, which they broke down into their most common building blocks of 120 secondary building units and 16 linkers.

By recombining these building blocks using about 750 different types of architectures, including many that are not usually included in such models, the researchers generated about 50,000 new MOF structures. They then used their computational models to predict how stable each of these structures would be and identified about 10,000 that they deemed ultrastable, both for thermal stability and activation stability.

The researchers also screened the structures for their “deliverable capacity” — a measure of a material’s ability to store and release gases. For this analysis, the researchers used methane gas, because capturing methane could be useful for removing it from the atmosphere or converting it to methanol. They found that the 10,000 ultrastable materials they identified had good deliverable capacities for methane and they were also mechanically stable, as measured by their predicted elastic modulus.

The researchers also identified certain building blocks that tend to produce more stable materials. One of the secondary building units with the best stability was a molecule that contains gadolinium, a rare-earth metal. Another was a cobalt-containing porphyrin, a large organic molecule made of four interconnected rings.

Students in Kulik’s lab are now working on synthesizing some of these MOF structures and testing them in the lab for their stability and potential catalytic ability and gas separation ability. The researchers have also made their database of ultrastable materials available for researchers interested in testing them for their own scientific applications.

The database of MOF structures developed in this work will be highly useful for researchers who are using computational screening to find new MOFs for targeted applications, says Randall Snurr, a professor of chemical and biological engineering at Northwestern University, who was not involved in the study. “Using machine learning methods they had previously developed, they were able to focus on generating MOF structures likely to have high stability, which is an important consideration for practical applications.”

The research was funded by the U.S. Defense Advanced Research Projects Agency, a National Science Foundation Graduate Research Fellowship, the Office of Naval Research, the Department of Energy, an MIT Portugal Seed Fund, and the National Research Foundation of Korea.

In conclusion, the use of machine learning in the development of ultrastable MOFs is a significant breakthrough that could have important implications for gas storage, separation, and conversion. The new computational model developed by the researchers enables a more efficient and cost-effective way of predicting MOF stability and properties, leading to the discovery of thousands of new ultrastable MOFs with potential applications in various scientific fields.

Deep Dive

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