Machine learning and natural language processing for the identification of synthesis parameters of NiMo sulfide catalysts

Aline Villarreal, Rodrigo Villarreal

TL;DR: Extraction of synthesis parameters of HDS catalysts using text mining

Keywords: Natural language processing, catalysis, text mining

Abstract: Catalysis is an interdisciplinary and complex field where several pieces of information must be put together to design a successful working catalyst. In recent years, theoreticians have contributed to accelerate the discovery of new catalytic materials by putting together information repositories like «Catalysis-Hub», but often the models address either the molecular or the engineering aspect of the reaction. Until now, the comparison in preparation methods is difficult even with materials prepared in the same laboratory. In this work, synthesis parameters for NiMo sulfide catalysts were extracted from existing literature adding specific algorithms to the ChemDataExtractor tool. It was found that natural language processing techniques can be used to extract information and gain knowledge from a great number of systems and allow to find hidden or misregarded links between preparation conditions.