预测波桑比率:半监督异常检测和监督方法的研究
Raheel Hammad1, Sownyak Mondal1
1Tata Institute of Fundamental Research Hyderabad, Hyderabad 500046, Telangana, India.
ACS omega
|January 15, 2024
概括
机器学习模型被开发用于识别罕见的辅助性材料并预测Poisson.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 机器学习应用 机器学习应用
背景情况:
- 辅助性材料,以负波桑比为特征,很少见,但具有独特的特性.
- 它们不寻常的机械行为为先进的材料设计提供了潜力.
- 发现和描述辅助性材料对于利用它们的应用至关重要.
研究的目的:
- 开发一种机器学习框架,用于检测辅助性材料.
- 为了预测Poisson的比率对于非auxetic材料.
- 确定影响辅助性行为的关键材料特征,并帮助检测异常.
主要方法:
- 实施了一种半监督的异常检测模型来识别辅助性材料.
- 创建了一个监督回归模型来预测Poisson比率.
- 来自回归模型的特征分析为异常检测提供了信息.
主要成果:
- 异常检测模型在识别辅助性材料方面实现了0.64的平均精度.
- 回归模型预测了Poisson比率的非auxetic材料的R平方值为0.82.
- 通过回归模型确定了异常检测的最佳特征.
结论:
- 开发的机器学习框架有效地检测到辅助性材料并预测Poisson的比率.
- 这种方法可用于发现具有其他罕见物理性质的材料.
- 这项研究强调了机器学习在材料发现方面的潜力.
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