Dataset: Machine Learning-Assisted Crystal Engineering of a Zeolite

Authors: Xinyu Li¹, He Han^1,2, Nikolaos Evangelou³, Noah J. Wichrowski⁴, Peng Lu³, Wenqian Xu ⁵, Son-Jong Hwang⁶, Wenyang Zhao ¹, Chunshan Song ², Xinwen Guo ², Aditya Bhan ¹, Ioannis G. Kevrekidis^3,4, Michael Tsapatsis ^1,3,7,8

Author affiliations:
¹:Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
²:State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China
³:Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
⁴:Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
⁵:X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA
⁶:Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
⁷:Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA
⁸:Institute for NanoBioTechnology, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA

Source Data for Li, X., Han, H., Evangelou, N. et al. Machine learning-assisted crystal engineering of a zeolite. Nat Commun 14, 3152 (2023). https://doi.org/10.1038/s41467-023-38738-5. Data here includes all raw data and an explanatory Excel document with eight sheets. All raw data are indexed by entry number from Li et al, 2023. Machine operable verisons of the summary sheet is also included. Codes applied for machine learning are provided in a zip folder.