通过机器学习方法对非金属晶体的带隙预测
Sadhana Barman1, Harkishan Dua1, Utpal Sarkar1
1Department of Physics, Assam University Silchar, Silchar 788011, Assam, India.
概括
机器学习模型可以预测材料带隙,这对于电子性质至关重要. 随机森林回归使用Seebeck系数和温度实现了97.55%的准确性,优于其他方法.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算材料科学科学 计算材料科学
背景情况:
- 带隙对于材料的电子结构至关重要,并对其热电性能产生重大影响.
- 具有大量带隙相关特征的广泛数据集的可用性使机器学习 (ML) 可用于预测建模.
研究的目的:
- 探索使用机器学习来预测非金属晶体的带隙.
- 确定与带隙相关的关键热电参数,并将其用于基于ML的准确预测.
主要方法:
- 用皮尔森相关性分析进行了带隙和42个热电参数之间的特征选择.
- 使用多种机器学习回归模型,包括多线性回归,多项式回归,随机森林回归和支持向量的回归.
- 每个模型的预测性能都基于带隙预测的准确性进行了评估.
主要成果:
- 皮尔森相关性分析确定了西贝克系数及其相应的温度作为与带隙高度相关的特征.
- 在测试的ML模型中,随机森林回归在预测材料带隙方面表现出卓越的性能.
- 随机森林回归实现了97.55%的R平方值,表明预测和实际带隙值之间存在强烈一致.
结论:
- 机器学习,特别是随机森林回归,为预测材料带隙提供了强大而准确的方法.
- 塞贝克系数和温度是开发有效的ML模型的关键特征,用于非金属晶体中带隙预测.
- 这项研究强调了ML在加速用于热电应用的材料发现和设计方面的潜力.
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