This looks interesting,
High-throughput experimentation (HTE) accelerates molecular discovery but struggles to quantify yields without pure analyte standards. The current need to purify standards increases cost, complexity, and cycle time, limiting the scalability of new molecular discovery. Here, we introduce an integrated “detect-predict” framework that leverages artificial intelligence (AI) for analyte standard-free yield quantification.
Paper is here https://doi.org/10.1021/jacsau.6c00812
Code https://github.com/xtalpi-inc/lcms2yield