Construction of an Interpretable Machine Learning Model for Yield Prediction and Mechanistic Elucidation Enabled by Global Reaction Route Mapping
In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.
In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.
Covalent organic frameworks (COFs) are versatile materials platforms for precise function integration owing to their high crystallinity, large surface areas, tunable characteristics and diverse and predictable structures. However, the dominant solvothermal method for COF synthesis requires harsh conditions, including high temperatures, toxic organic solvents, sealed and pressurized reactors, and extended reaction times that often exceed several days.
In modern pharmaceutical and bioprocess manufacturing environments, automation alone is no longer enough. True digital transformation requires every instrument, robot, sensor, and workflow to operate with precision, reliability, and complete data integrity.
Chemspeed is a global team committed to enable automated and digitalized workflows for scientists in R&D and QC.
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