AI and automation are speeding up science and chemistry by helping scientists pick which experiments to conduct and home in on promising new materials.
It took two to three months, compared to years of experiments typically required to find a new alloy, says Ziyuan Rao, a postdoctoral researcher at the Max Planck Institute for Iron Research in Germany and a co-author of the paper, published Thursday in the journalIt's much more difficult to train AI models to predict how to synthesize a material, in part because there isn't data on what can't be synthesized, says Keith Butler, a senior lecturer at Queen Mary University of London.
The high cost in time and money to conduct experiments means less data is available to train AI systems — and much of what is available is collected in different experiments or under various conditions, and is spread across institutions orThe alloy work is "impressive" because they were able to get the results with sparse data, says Isayev, who uses AI to identify new materials and predict the properties of chemicals for solar energy technology and drug design.
"I think that next year that's what's going to be very hot in material sciences and in physical sciences," says Gabriel Gomes, a professor at CMU who uses machine learning to develop new chemical reactions and catalysts.
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