This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Machine learning doesn't replace human intelligence, but it can outlast human endurance, which makes it a helpful tool for chemistry and materials discovery. Scientists know machine learning models can make predictions based on the vast reams of data they are trained on, but can they take it a step further and massively scale up testing those predictions?
"We already see that AI is powerful in terms of predicting new structures," said Zhiling Zheng, an assistant professor of chemistry in Arts & Sciences at Washington University in St. In a recent essay for the journal Science, Zheng proposes how artificial intelligence (AI) systems can tackle that next step: "At the heart of this platform is the AI's ability to read chemistry like a chemist," Zheng wrote in the essay. Christopher Cooper, at the WashU McKelvey School of Engineering, is on the same page.
He recently published a paper in the journal Matter documenting how to curate troves of data for polymer synthesis. "Data curation" is the first major step of this work. The data needs to be collected and converted to a form that machine learning models can easily digest, as Cooper and Zheng explained.
The machines have been fed a full diet of the known rules of chemistry. That's how they make their predictions. What's missing is the application of those rules, following through to run simulations on making those molecules.
For that part, the models need the "recipes" of chemical synthesis—the instructions buried in journals, textbooks and footnotes over the century—and that is what must be collected, translated and "fed" to them. "You want a model to be able to understand those instructions, mash them together and say, 'this is higher likelihood of being successful,'" said Cooper, an assistant professor of energy, environmental and chemical engineering. Zheng comes from a background in studying metal-organic frameworks (MOFs), which are built with metal ions as nodes and organic blocks as linkers.
A MOF is shaped like a cube with metallic elements on each corner, lending itself to endless tinkering and potential uses. The problem is, it's too open-ended. "There are just so many different possibilities," Zheng said.
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