Sifting Through Multisource Data for Safer Battery Materials with Machine Learning
 

Stanford researchers analyzed more than 12,000 solid electrolytes and trained a machine learning model that is three to five times more likely than a scientist to identify a promising new material.

2018-10-10 08:00 AM


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