Thanks to machine learning, the future of catalyst research is now To date, research in the field of

Thanks to machine learning, the future of catalyst research is now To date, research in the field of combinatorial catalysts has relied on serendipitous discoveries of catalyst combinations. Now, scientists from Japan have streamlined a protocol that combines random sampling, high-throughput experimentation, and data science to identify synergistic combinations of catalysts. With this breakthrough, the researchers hope to remove the limits placed on research by relying on chance discoveries and have their new protocol used more often in catalyst informatics.Catalysts, or their combinations, are compounds that significantly lower the energy required to drive chemical reactions to completion. In the field of combinatorial catalyst design, the requirement of synergy—where one component of a catalyst complements another—and the elimination of ineffective or detrimental combinations are key considerations. However, so far, combinatorial catalysts have been designed using biased data or trial-and-error, or serendipitous discoveries of combinations that worked. A group of researchers from Japan has now sought to change this trend by trying to devise a repeatable protocol that relied on a screening instrument and software-based analysis.Read more. -- source link

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