机器学习和在氧化物中具有高介电电容性的原子起源
Yuho Shimano1, Alex Kutana1, Ryoji Asahi2
1Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
Scientific reports
|December 14, 2023
概括
机器学习模型使用结构数据准确地预测材料中的介电电容. 这加速了用于储能和电子的新材料的发现.
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
- 固态物理 固态物理
背景情况:
- 发现具有高介电电容性的稳定材料对于先进的储能和电子技术至关重要.
- 理论和计算方法对于理解材料属性和指导发现至关重要.
- 虽然ab initio方法是准确的,但它们对于快速选材料来说太慢了.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于从材料结构中预测介电常数.
- 确定有助于高介电电容性的关键结构和组成特征.
- 为了加速发现新的高性能介电材料.
主要方法:
- 创建一个包含各种氧化物的介电性质的数据库.
- 训练和测试随机森林和图形卷积神经网络 (GCNN) 模型.
- 分析特征重要性,缩小维度和描述符的集群.
主要成果:
- 随机森林和GCNN模型在预测介电常数方面表现相似.
- 基于结构信息,ML模型成功预测了材料反应.
- 特性重要性分析确定了与高介电电容度相关的特定局部几何特征.
结论:
- 机器学习模型可以有效地从结构数据中预测介电电容量,从而实现快速的材料选.
- 该研究强调了当地的几何和组成特征对于实现高介电电容性的重要性.
- 这种方法加速了用于储能和电子应用的新材料的设计和发现.
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