Machine–Learning–Assisted synthesis for Efficiently of Synthesis Condition for Metastable Novel Metal–Organic Frameworks (MOFs) Using Failed Experiments

Abstract

Lanthanide based Metal–Organic Frameworks (Ln–MOFs) have been widely studied for luminescence and sensor application. It is well known that synthesis of metal complexes with multi components have been suffered from many crystal polymorphisms depending on the reaction environment because various reaction intermediates also exist under the synthesis condition and the synthetic process using the same reactants. Moreover, Ln–MOFs generally provide many crystal polymorphisms because lanthanide ions show many flexible coordination geometry, resulting in isostructural crystal structure regardless of different metal ions. Therefore, it is difficult to selectively synthesize crystal polymorphs, and synthesis guidelines have not been established. In this study, we try to extract the dominate factors of the synthesis by using two machine learning techniques. We performed solvothermal synthesis by using four kinds of lanthanide metal ions and terephthalic acid and examined the various reaction condition. As a result, we have found an unknown phase 1 when cluster analysis was conducted on synthesis results, and the synthesis condition was explored by using decision tree. We found that the reaction condition to synthesize unknown phase 1 showed strong dependency on reagent purity.