Generative artificial intelligence added 120,000 recipes for “muff” materials that can capture carbon (artificial intelligence image)
Imagine that you are trying to come up with a new recipe for a delicious cake, and instead of working on your own you turn to a magical cookbook that can create thousands of unique recipes with different combinations of ingredients, flavors and textures.
Likewise, researchers from the US Department of Energy's Argonne National Laboratory, in a study published in the journal Communications Chemistry, were able to employ generative artificial intelligence to produce more than 120,000 new "recipes" for candidate materials for carbon capture, which are known as "metal organic frameworks." , or what is known as “muff” materials, then they used “machine learning” to reduce this number of recipes to a smaller number that is likely to be more effective, then they tested these candidate recipes to determine the most efficient ones, and finally they used a computer program to find out the secret of synthesizing the “muff” materials recipes. “Most efficient in carbon capture.
The discovery of these materials dates back to 1999, and Omar Yaghi, an American chemist of Jordanian origin who was born in Amman in 1965, is considered one of the most prominent scientists who published research on them.
It has a very ordered structure, and relies on linking organic and inorganic building blocks to form porous structural structures such as a network. The pores in this network can be stable enough to store molecules of a substance, which makes it have many applications such as storing gases, extracting pollutants, or as materials to catalyze chemical reactions or Delivering medications into the body.
Crystal structure of the top 6 MOV materials created by Artificial Intelligence (Communications Chemistry)
4 advantages of mauve materials
Mauve materials in this way appear to be strong candidates for carbon sequestration, thus reducing greenhouse gas emissions from power plants and other industrial facilities.
This is due "theoretically" to 4 advantages mentioned by studies that dealt with the use of these materials, which are:
High surface area:
These materials have an incredibly high surface area relative to their size, and this large surface area provides ample space for gas molecules such as carbon dioxide to be absorbed onto the surface.
Tunable pore size:
Researchers can design and synthesize these materials with specific pore sizes designed to capture specific molecules. This tunability allows for the selective absorption of gases such as carbon dioxide while excluding other gases.
Chemical diversity:
These materials can be chemically modified to enhance carbon capture properties, and researchers can adjust the composition of the organic and inorganic building blocks to improve selectivity for carbon over others, as well as improve carbon absorption capacity.
Reusability:
Movable materials can often be renewed and reused multiple times without significantly losing their ability to selectively absorb carbon. This makes them cost-effective and environmentally friendly compared to single-use carbon capture materials.
Three building blocks make the task difficult
These materials contain three types of building blocks in their molecules:
Metal nodes (metal ions or groups):
Atoms or groups of metal ions that act as stabilizers, holding the structure together. Various metals such as zinc, copper, or chromium can be used for this purpose.
Organic bonds (organic molecules):
molecules made of carbon, hydrogen, and other elements that link metal nodes.
Inorganic materials:
These are additional building blocks that can be incorporated into some MOFs, providing greater stability or functionality, such as other types of metal ions or groups, or even non-metallic atoms such as oxygen or nitrogen.
These three components can be arranged in different relative positions and configurations, and as a result there are countless possible configurations of the “MOV” materials, but what is required is to choose the most effective materials in carbon capture, and this is where the artificial intelligence used by the researchers comes into play.
Researchers used 4 paths of artificial intelligence to design carbon capture materials (AI image)
4 paths to artificial intelligence
Through four different paths, the new study explains how researchers succeeded in employing artificial intelligence to choose the most effective materials, which are as follows:
Generative Artificial Intelligence:
The researchers began by using a computer program that could come up with new combinations of the basic elements of mauve materials, just like a magic cookbook that can create thousands of new recipes for making a delicious cake. In just 30 minutes, the researchers produced more than 120,000 new “recipes.”
Machine Learning:
Let's say you want to know which cake recipes will taste the best. In this case, you can collect feedback from your friends and family about the cakes they've enjoyed before. Your Magic Cookbook can learn from this data to predict which recipes are likely to be tastiest. Likewise, the researchers used machine learning to analyze data from previous experiments and simulations to predict the most efficient CO2 trapping material designs out of the 120,000 recipes.
High-yield screening:
Once you have a bunch of cake recipes, you won't have time to bake and taste each one individually, so
You decided to bake small samples of several different types of cakes at once and quickly tested them to see which ones were the tastiest. This is similar to the high-throughput screening that the researchers conducted, where they tested many different mauve candidates, identified 6 that seemed to be the most efficient, and then subjected them to For further research.
Molecular dynamics simulation:
Finally, let's say you want to understand exactly how the ingredients in the best cake recipe interact with each other at the molecular level to create delicious flavor and texture, and you resort to using computer simulations to give you a visualization and analysis of the movements and interactions between the molecules of each ingredient. Likewise, the researchers used computer simulations to give you a visualization and analysis of the movements and interactions between the molecules of each ingredient. Molecular dynamics to study how the atoms in the best candidate materials behave, and whether they can effectively trap carbon dioxide molecules.
How to incorporate MOV materials into industrial processes for carbon capture is one of the questions that researchers need to address (Reuters)
A wider lens.. 4 questions
This AI-driven mechanism holds the promise of producing a MOV material that could be good at carbon capture, cost-effective, and easy to produce, Argonne Laboratory scientist Elio Huerta, who helped lead the study, said in a press release from Argonne National Laboratory. To the US Department of Energy.
“An AI-enabled MOV materials design approach will allow us to get what Ian Foster, Argonne’s chief scientist and director of data science and learning, calls a ‘broader lens’ on these types of porous structures,” he adds.
Although Khaled Gad, professor of materials science at the Egyptian University of Illuminia, is enthusiastic about the mechanism used by Huerta and his peers, he believes that there are four questions that must be answered in subsequent studies to ensure that materials created by artificial intelligence will be effective when moving to industrial application.
In a telephone interview with Al Jazeera Net, Jad details the four questions as follows:
Scalability:
How can production of new MOF materials be scaled up to industrial levels, and how can we make the process more cost effective?
Researchers will therefore need to conduct further studies into scalable material synthesis techniques, cost-effective raw materials and recycling methods for used materials.
Long-term stability and durability:
How do MOF materials perform under real-world conditions over long periods?
This is crucial, as studying the long-term stability and durability of materials in diverse environments, including high temperatures, humidity and exposure to chemicals, is essential to evaluate their practical feasibility.
Integration with industrial processes:
How can MOF materials be seamlessly integrated into existing industrial processes for carbon capture?
Collaboration with industry partners to pilot MOF-based carbon capture technologies and evaluate their compatibility with industrial infrastructure will be essential for real-world implementation.
Environmental Impact:
What are the environmental impacts of the production, use and disposal of mauve materials?
There is a need to assess the environmental footprint of new MOV materials.
Source: Al Jazeera + websites