Mining Insights on Metal-Organic Framework Synthesis from Scientific Literature Texts

TL;DR

This study leverages machine learning and rule-based algorithms to extract synthesis data from thousands of MOF papers, enabling accurate prediction of synthesis conditions and accelerating MOF discovery.

Contribution

It introduces a large-scale data mining approach combined with predictive algorithms to determine MOF synthesis conditions from scientific literature.

Findings

Achieved 90.3% F1 score in extracting synthesis parameters.

Predicted successful MOF synthesis with 83.1% accuracy.

Correctly identified low synthesizability in amorphous MOFs.

Abstract

Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. Trial-and-error approach that relies on a chemist's intuition and knowledge has limitations in efficiency due to the large MOF synthesis space. To this end, 47,187 number of MOF were data mined using our in-house developed code to extract their synthesis information in 28,565 MOF papers. The joint machine learning/rule-based algorithm yields an average F1 score of 90.3 % across different synthesis parameters (i.e. metal precursors, organic precursors, solvents, temperature, time, composition). From this data set, a PU learning algorithm was developed to predict synthesis of a given MOF material using synthesis conditions as inputs, and this algorithm successfully predicted successful synthesis in 83.1 % of the synthesized data in the test set. Finally, our model correctly predicted three amorphous MOFs (with their representative experimental synthesis condition) as having low synthesizability scores while the counterpart crystalline MOFs showed high synthesizability scores. Our results show that big data extracted from the texts of MOF papers can be used to rationally predict synthesis conditions for these materials, which can accelerate the speed in which new MOFs are synthesized.