基于深度神经网络的铁电材料介电性能的预测
Jiachen Wang1,2, Ziyu Cui2, Xin Zhang2
1Department of Neurosurgery, Zhumadian Central Hospital, Affiliated Hospital of Huanghuai University, Zhumadian, China.
Science progress
|February 18, 2025
概括
深度神经网络 (DNN) 预测铁电材料的特性,克服组合和处理中的随机性. 这加速了新材料的发现,并优化了现有的铁电陶性能.
科学领域:
- 材料科学与工程 材料科学与工程
- 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:
- 铁电材料具有独特的电气性能,对于先进的应用至关重要.
- 材料组成,微观结构和加工引入了随机性,使性能预测复杂化.
- 传统方法难以捕捉复杂的相互作用,阻碍了材料优化.
研究的目的:
- 开发一种新的深度神经网络 (DNN) 方法来预测铁电材料的电特性.
- 建立一个可靠的代理模型来估计介电常数和介电峰值.
- 通过对 (1-x) Na0.5Bi0.5TiO3-xSrZrO3陶的实验数据验证DNN模型的准确性.
主要方法:
- 用于合成 (1-x) Na0.5Bi0.5TiO3-xSrZrO3陶的固态反应方法.
- 试验测量相位结构和电性质.
- 用实验数据训练DNN,以创建一个预测模型.
主要成果:
- 该DNN模型准确地捕捉了成分,加工和微观结构对电气性能的影响.
- 预测值显示了与实验结果的可接受差异 (绝对误差<5).
- 评估指标 (MAPE,SMAPE,R2) 证实了该模型的实用性和可靠性.
结论:
- 开发的DNN模型有效地预测铁电性质,解决固有的材料不确定性.
- 这种方法加速了新型铁电材料的发现.
- 该模型增强了现有铁电材料性能的优化.
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