Brgoch Superhard Materials Training Data

2574 (2537) materials used for training regressors that predict shear and bulk modulus. The xlsx file provided consists of the original data used to train models described in reference 1 below. The json.gz file includes structural and composition based data from the Materials Project as well as mpid values. 37 entries have been removed from this file as they could not be properly cross referenced on the Materials Project database. An additional 48 values have been marked as suspect due to large discrepancies in shear and bulk modulus from the source file and current MP values.<br>Data is available as Monty Encoder encoded JSON and as a XLSX 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 Directed Search for Ultraincompressible, Superhard Materials Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch Journal of the American Chemical Society <strong>2018</strong> <em>140</em> (31), 9844-9853 DOI: 10.1021/jacs.8b02717 <br>

本数据集共包含2574（有效样本2537）种材料，用于训练可预测剪切模量与体积模量的回归模型。<br>本次提供的XLSX文件包含了下文参考文献1中所述模型的训练原始数据。<br>json.gz文件则包含来自材料项目（Materials Project）的结构与组分相关数据，以及mpid值。该文件中已剔除37条无法在材料项目数据库中完成有效交叉验证的条目；另有48条数据因源文件与当前材料项目（MP）的剪切模量、体积模量数值存在显著偏差，被标记为可疑数据。<br>本数据集提供两种存储格式：Monty Encoder编码的JSON文件与XLSX文件。推荐使用matminer Python库的datasets模块进行数据读取。<br>引用说明：若您在研究中使用本数据集并需标注引用，请务必引用下文所列的原始文献，请勿仅引用本数据集页面，也无需同时引用本页面与原始文献。<br>本数据集相关研究发表于：<br>《面向超不可压缩超硬材料的机器学习定向搜索》，作者：Aria Mansouri Tehrani、Anton O. Oliynyk、Marcus Parry、Zeshan Rizvi、Samantha Couper、Feng Lin、Lowell Miyagi、Taylor D. Sparks、Jakoah Brgoch，发表于《美国化学会志》2018年，第140卷第31期，页码9844-9853，DOI: 10.1021/jacs.8b02717

搜集汇总

背景与挑战

背景概述

该数据集包含2574种材料的训练数据，用于预测剪切模量和体积模量，支持Excel和JSON格式，并与超硬材料机器学习研究相关。