在多项实验验证研究中使用的材料化学中的机器学习描述符: Oliynyk元素性质数据集
Sangjoon Lee1, Clio Chen2, Griheydi Garcia2
1Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, United States.
Data in brief
|February 22, 2024
概括
一个由98个基本特征组成的新数据集通过改进固态材料的机器学习模型来帮助材料信息学,特别是在有限的数据的情况下. 这种组合特征集增强了属性预测和材料发现.
科学领域:
- 材料 信息学 信息学
- 计算材料科学科学 计算材料科学
- 机器学习在材料科学中的应用
背景情况:
- 材料信息学依赖于数据驱动的材料分析和发现方法.
- 有效的机器学习 (ML) 模型需要针对特定应用程序量身定制的准确特征 (描述器).
- 一般化学特征剂通常在固态材料属性预测方面表现不佳.
研究的目的:
- 引入Oliynyk属性清单,这是对固态材料组成特征的精心策划的数据集.
- 为了证明这个功能在有限的数据集 (50-1000个数据点) 上设置的有效性.
- 为了促进对固态材料的ML模型中改进的特征属性映射.
主要方法:
- 组装了一组数据集,其中包括热力学,电子,大小,电子阴性和散量属性等98个原子的元素特征.
- 从公共数据库和文献中编译数据,并补充了高斯过程回归插值.
- 在财产预测任务中使用了ML模型 (SVM,随机森林,SVR).
主要成果:
- 奥利尼克物业清单在固态领域的有限数据集上表现强.
- 该数据集已成功应用于同行评审的研究,用于预测材料硬度,分类,Heusler化合物发现,带隙预测和位置偏好.
- 生成的特性使得ML模型的性能可靠,即使数据稀少.
结论:
- 奥利尼克属性列表是固态材料信息学中构成特征生成的宝贵资源.
- 这一功能集显著提高了各种材料科学应用的ML模型的准确性和可靠性.
- 该数据集支持高效的材料发现和属性预测,特别是当数据稀缺时.
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