<p>Machine
learning (ML) has brought significant technological innovations in many fields,
but it has not been widely embraced by most researchers of natural sciences to
date. Traditional understanding and
promotion of chemical analysis cannot meet the definition and requirement of
big data for running of ML. Over the years, we focused on building a more
versatile and low-cost approach to the acquisition of copious amounts of data
containing in a chemical reaction. The generated data meet exclusively the
thirst of ML when swimming in the vast space of chemical effect. As
proof in this study, we carried out a case for acute toxicity test throughout
the whole routine, from model building, chip preparation, data collection, and
ML training. Such a strategy will probably play an important role in connecting
ML with much research in natural science in the future.</p>
