A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

[EN] Zeolites are porous, aluminosilicate materials with many industrial and "green" applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite's framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 angstrom(3) , and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies.We would like to acknowledge funding from the National Science Foundation Award No. 1534340, DMREF that provided support to make this work possible, support from the Office of Naval Research (ONR) under Contract No. N00014-16-1-2432, and the MIT Energy Initiative. Early work was collaborative under the Department of Energy Basic Energy Science Program through the Materials Project under Grant No. EDCBEE. This work has also been supported by the Spanish Government through the Severo Ochoa Program SEV-2016-0683 and the Grant No. MAT2015971261-R, and by La Caxia Foundation through the MIT-SPAIN SEED FUND Program (LCF/PR/MIT17/11820002).Jensen, Z.; Kim, E.; Kwon, S.; Gani, TZ.; Román-Leshkov, Y.; Moliner Marin, M.; Corma Canós, A.... (2019). A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science. 5(5):892-899. https://doi.org/10.1021/acscentsci.9b00193S8928995