Chemical diversity organised with machine learning

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Machine learning is being the key to everything we need in the modern world. And, now it is being the top most things when it comes to doing something delicate and amazing at the same time.

Because of the popularity of MOFs, scientists are developing, synthesizing, and cataloging to know whether it is new and not some minor variation of a structure that has already been synthesized. In order to address the problem, EPFL scientists, in collaboration with MIT, have used machine-learning to organize the chemical diversity found in the ever-growing databases for the popular metal-organic framework materials. Using machine-learning, scientists developed a “language” to compare two materials and quantify their differences.

Chemical diversity organised with machine learning
Computational Chemistry Highlights

Scientists have all set to determine the chemical diversity in MOF databases through this new language.

“Before, the focus was on the number of structures. But now, we discovered that the major databases have all kinds of bias towards particular structures. But now we discovered the major databases have all kinds of bias towards particular structures. There is no point in carrying out expensive screening studies on similar structures. One is better off in carefully selecting a set of very diverse structures, which will give much better results with far fewer structures,” said Professor Berend Smit at EPFL.

Another exciting and amazing example of this is the scientific archaeology; the researchers used their machine-learning system to identify the MOF structures that; at the time of the study, were published as very different from the ones that are already known.

Smit also added, “So we now have a straight forward tool that can tell an; experimental group how different their novel MOF; is compared to the 90,000 other structures already reported.”


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