期刊
ACS CENTRAL SCIENCE
卷 5, 期 5, 页码 892-899出版社
AMER CHEMICAL SOC
DOI: 10.1021/acscentsci.9b00193
关键词
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资金
- National Science Foundation [1534340]
- Office of Naval Research (ONR) [N00014-16-1-2432]
- MIT Energy Initiative
- Department of Energy Basic Energy Science Program [EDCBEE]
- Spanish Government through the Severo Ochoa Program [SEV-2016-0683]
- Spanish Government [MAT2015971261-R]
- La Caxia Foundation through the MIT-SPAIN SEED FUND Program [LCF/PR/MIT17/11820002]
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1534340] Funding Source: National Science Foundation
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.
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