Machine learning could change the empirical side of catalyst development

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Greetings dear friends. Many times in research, combinations are made that are difficult to generalize, and in the field of catalysis, many catalysts have been fortuitous discoveries, so the empirical aspect of research in this area of chemistry dominates the literature, basically trial and error tests. But one group of researchers is changing the way catalysts are developed by combining random sampling, experimentation and machine learning to identify synergistic combinations of catalysts.

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Machine learning could change the way catalysts are developed. Image edited by @emiliomoron, original from pixabay.com.

With this method, the researchers hope to shorten evaluation times and save the resources that are employed in this type of research, which relies heavily on random combinations.

Catalysts are chemical species capable of accelerating a chemical reaction, but not any chemical element turns out to be a catalyst, and a good catalyst for one reaction is not good for another, so there is a lot of trial and error in this type of research. On the other hand, there are chemical species that by themselves do not modify any parameter of chemical reactions, but when combined with others, one component complements the other, and the synergy between the two results in a good catalyst. So in the field of combined catalyst development, the synergy between the elements is the key, so it is very important to eliminate any type of combination that is not effective.

So far, in the field of catalysis this process of discarding inefficient combinations is done by experimentation, there are no equations or chemical laws that predict whether, for example, the Pt-Ni combination will be more efficient than the W-Ni combination in the hydrogenation reaction of a hydrocarbon. So before testing a combination, it is essential to gather all the information available in the literature, which is usually also biased by data from accidentally found combinations.

But this could change thanks to a new study recently published in the journal ACS Catalysis. This study details the identification of potentially effective combinations using a protocol based on a high-throughput screening instrument and software analysis; random samples of 300 solid catalysts from a universe of more than 36,000 catalysts for oxidative coupling of methane were evaluated using this procedure. Evaluating such a large number is almost impossible for humans, so the team designed this procedure to facilitate the study of the reaction. And with the obtained data set, free of bias, was used to design the new protocol, which serves as a guide for the development of new catalysts.

A form of decision tree classification was implemented in the software, which is widely used for the machine to understand how the selected combinations influence the performance of the catalysts, which helped to obtain the necessary guidelines for the design of the new catalysts. With random sampling, 51 catalysts out of 300 provided sufficiently superior C2 reaction performance to the non-oxidative non-catalytic process of the reaction.

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Catalyst design guideline scheme. Source: Image designed by @emiliomoron, contains public domain image.

Decision tree classification was successfully implemented, facilitating efficient sampling of catalysts toward improved reaction performance. It demonstrates the importance of tools that help researchers to find synergistic combinations without bias, in the study and design of new catalysts, allowing to approach these studies with a less empirical approach, thus allowing to perform such demanding studies in more realistic time frames and optimizing resources.

Well friends, I hope you found the information interesting, which shows us how advances in autonomous learning are impacting various areas of scientific research. see you next time!




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13 comments
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I once told a friend that very soon, technology will take over almost every aspect of human endeavour. Here we are, incorporating machine learning (which is part of the technology of AI - Artificial Intelligence) in chemical reactions.
Nice and informative piece as usual buddy

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You were right back then my friend, we are seeing more and more AI support in many tasks, soon they could be in practically all of them. Glad you liked the post, best regards.

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Hello @emiliomoron
Once again it is demonstrated how technological advances every day help human beings to achieve results unimaginable decades ago.
Excellent reading, thanks for sharing.
Best regards.

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Thanks to you for stopping by to read, my friend. No doubt that a few decades ago we could not have imagined that machines would help us to solve this kind of problems. Greetings!

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Hello @emiliomoron
Catalysis is an important component in the industrial production process. Managing to find those two components that make synergy is important point to perform the processes effectively.

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That's right friend @josevas217, in the industry a high percentage of the processes are catalyzed, so finding good catalysts for the processes is something very important. Greetings my friend.

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Hello friend @emiliomoron

The advance of science, is almost exclusively a product of fortuitous behaviors, where trial and error embellish and give meaning to everything we try to know. Little or I understand these topics about catalysts, but reading your posts makes the topic digestible. Greetings and be well.

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Hello friend @lupafilotaxia, really trial and error add an interesting component to experimentation, although there are cases where it is good to have a good guidance that puts us on the right path. I'm glad you can appreciate the information even though it's not your area, best regards my friend.

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It is very good news that scientists are now applying AI (artificial intelligence) to speed up the process of finding more efficient substances and catalysts, industries such as oil can benefit from better processes, as well as medicine among others, in my point of view. It's about time more consideration was given to this science to drive technological advances.

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That's right my friend, the oil, pharmaceutical and chemical industry in general will benefit from this type of advance, so no doubt we will see that more and more AI will be taken into account in various areas of research. Greetings!

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Another interesting part where machine learning can provide its contributions. Greetings my dear friend and thank you for your contribution.

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