Glass Ternary High Throughput Data

Metallic glass formation dataset for ternary alloys, collected from the high-throughput sputtering experiments measuring whether it is possible to form a glass using sputtering.<br><br>The hipt experimental data are of the Co-Fe-Zr, Co-Ti-Zr, Co-V-Zr and Fe-Ti-Nb ternary systems.<br>Available as Monty Encoder encoded JSON and as CSV. Recommended access method is with the matminer Python package using the datasets module.<br>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.<br>Dataset discussed in:<br>Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments By Fang Ren, Logan Ward, Travis Williams, Kevin J. Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta <em>Science Advances</em> 13 Apr 2018 : eaaq1566本数据集为三元合金（ternary alloys）金属玻璃（metallic glass）形成数据集，采集自高通量溅射实验（high-throughput sputtering experiments）——该实验通过溅射工艺测试合金是否可形成金属玻璃。<br><br>本高通量实验数据涵盖Co-Fe-Zr、Co-Ti-Zr、Co-V-Zr及Fe-Ti-Nb四个三元合金体系。<br>该数据集提供Monty Encoder编码的JSON格式与CSV格式文件，推荐通过matminer Python包的datasets模块进行访问。<br>引用须知：若本数据集对您的研究有所助益并需在成果中引用，请务必引用其原始来源，请勿仅引用本数据集页面，也不要同时引用本页面与原始文献，而应直接标注原始来源。<br>本数据集相关研究论文为《通过机器学习与高通量实验迭代加速发现金属玻璃》，作者为Fang Ren、Logan Ward、Travis Williams、Kevin J. Laws、Christopher Wolverton、Jason Hattrick-Simpers、Apurva Mehta，刊载于《科学进展（Science Advances）》，2018年4月13日，文章编号eaaq1566