识别基于材料化学的机器学习预测的高可靠性区域
Evan M Askenazi1, Emanuel A Lazar2, Ilya Grinberg1
1Department of Chemistry, Bar-Ilan University, Ramat, Gan 52900, Israel.
Journal of chemical information and modeling
|November 20, 2023
概括
在材料科学中,可靠的机器学习 (ML) 预测需要了解模型的局限性. 在特征空间中凸的船体有助于识别可靠的ML预测,并提取物理洞察力,特别是对于狭窄的材料类.
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 机器学习
背景情况:
- 材料设计中的机器学习 (ML) 应用正在迅速发展.
- 一个关键的挑战是了解ML预测的可靠性,特别是在材料科学中常见的小数据集.
研究的目的:
- 开发用于评估材料科学中ML预测可靠性的方法.
- 展示如何识别可靠的预测区域,并从ML模型中提取物理理解.
主要方法:
- 利用ML预测透明导体氧化物的形成能量和带隙,稀释溶液扩散和矿性质.
- 在特征空间中构建一个凸起的船体,以界定可靠的ML预测区域.
- 分析了凸船体内的系统,以提取物理洞察力.
主要成果:
- 在特征空间中凸的船体有效地识别了具有高度可靠的ML预测的区域.
- 对封闭系统的分析产生了有价值的物理理解.
- 服从物理原理的材料很可能是相似的,并且显示出强大的特征-属性关系.
- 在训练数据中包含不同的材料类并不能提高准确性;狭窄,相似的材料类产生可靠的ML结果.
结论:
- 凸体船体方法是验证材料科学中的ML预测的强大工具.
- 将ML模型集中在类似材料的狭窄类别上,提高了预测可靠性.
- 将物理原理与ML集成,可以改善材料设计和发现.
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