Machine intelligence for designing molecules and reaction pathways
Summary
The team, at the Institute of Statistical Mathematics (ISM) in Tokyo, published their results in the journal Science and Technology of Advanced Materials: Methods. The greatest impediment has been the technical difficulties in finding chemical reactions that can make the designed molecules with efficiencies and costs that could be practicable for real-world uses. “Our novel machine learning algorithm and associated software system can design molecules with any desired properties and suggest synthetic routes for making them from an extensive list of commercially available compounds,” says statistical mathematician Ryo Yoshida, leader of the research group. The process uses a statistical approach called Bayesian inference which works with a vast set of data about different options for starting materials and reaction pathways. Many of the most important industrial and biological compounds are polymers, but it has proved difficult to make new versions proposed by machine learning due to challenges in finding reactions to build the designs.