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Glass Binary Data
Version 2 2018-11-14, 00:02
Version 1 2018-11-05, 19:06
dataset
posted on 2018-11-14, 00:02 authored by Y. T. Sun, H. Y. Bai, M. Z. Li, W. H. Wang, Hacking MaterialsHacking MaterialsMetallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying. This dataset covers all compositions with an interval of 5 at.% in 59 binary systems, containing a total of 5959 alloys in the dataset. The target property of this dataset is the glass forming ability (GFA), i.e. whether the composition can form monolithic glass or not, which is either 1 for glass forming or 0 for non-full glass forming.
The V2 versions of this dataset have been cleaned to remove duplicate data points. Any entries with identical formula and both negative and positive GFA classes were combined to a single entry with a positive GFA class.
Data is available as
Monty Encoder encoded JSON and as the source CSV file. Recommended access method is with
the matminer Python package using the datasets module.
Note on citations: If you found this dataset useful and would like to
cite it in your work, please be sure to cite its original sources below
rather than or in addition to this page.
Dataset discussed in:
Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability
† Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
‡ Department of Physics, Beijing Key Laboratory of Optoelectronic Functional Materials &
Micro-nano Devices, Renmin University of China, Beijing 100872, People’s Republic of China
§ University
of Chinese Academy of Science, Beijing 100049, People’s
Republic of China
J. Phys. Chem. Lett., 2017, 8 (14), pp 3434–3439
DOI: 10.1021/acs.jpclett.7b01046
Publication Date (Web): July 11, 2017