JARVIS ML Training Data

Various properties of 24,759 bulk and 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database. <br><br>This dataset was modified from the JARVIS ML training set developed by NIST (1-2). The custom descriptors have been removed, the column naming scheme revised, and a composition column created. This leaves the training set as a dataset of composition and structure descriptors mapped to a diverse set of materials properties.<br><br>Available as Monty Encoder encoded JSON and as the source Monty Encoder encoded JSON file. 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>Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape Kamal Choudhary, Brian DeCost, and Francesca Tavazza Phys. Rev. Materials <b>2</b>, 083801<br>Original Data file sourced from:choudhary, kamal (2018): JARVIS-ML-CFID-descriptors and material properties. figshare. Dataset.

从JARVIS密度泛函理论（Density Functional Theory, DFT）数据库中获取的、采用OptB88vdW与TBmBJ泛函计算得到的24759块体及二维材料的各类性质。

本数据集源自美国国家标准与技术研究院（National Institute of Standards and Technology, NIST）构建的JARVIS机器学习（Machine Learning, ML）训练集，并经修改得到。本次修改移除了自定义描述符，修订了列命名规则，并新增了成分列，最终得到的训练集为一类将成分与结构描述符映射至多样材料性质的数据集。

本数据集可通过Monty Encoder编码的JavaScript对象表示法（JSON）格式以及原始Monty Encoder编码JSON文件获取，推荐使用matminer Python库的datasets模块进行访问。

引用说明：若您认为本数据集具有使用价值并希望在研究中引用它，请务必引用其下方列出的原始文献，而非仅引用本页面。

本数据集相关研究发表于：《基于力场启发式描述符的材料机器学习：快速筛选与能量景观映射》，作者Kamal Choudhary、Brian DeCost与Francesca Tavazza，《物理评论材料（Physical Review Materials）》，2卷，083801。

原始数据集文件来源：Choudhary, Kamal（2018）：《JARVIS-ML-CFID描述符与材料性质》，figshare，数据集。