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基于构成的机器学习,用于预测和设计4+化的
Ngo T Que1, Vu D Huan2, Le T Duy2
1Phenikaa Institute for Advanced Study, Phenikaa University Hanoi 12116 Vietnam anh.phanduc@phenikaa-uni.edu.vn.
RSC advances
|March 2, 2026
概括
本研究引入了一种数据驱动的方法,以仅使用元素组成来预测Mn4+化的光学特性. 这种方法可以有效地发现新的发光材料.
科学领域:
- 材料科学 材料科学 材料科学
- 固态化学 固态化学
- 计算材料科学科学 计算材料科学
背景情况:
- 预测的光学特性对于照明和显示应用至关重要.
- 现有的方法往往需要复杂的结构信息,限制了快速的材料发现.
- 在需要窄带红色发射的应用中,4+合很重要.
研究的目的:
- 开发一个数据驱动的模型,用于预测Mn4+化的激发/发射波长和晶体场能量水平.
- 为模型培训建立Mn4+激活的最大实验数据集.
- 为了使光器的反向设计基于所需的光学输出.
主要方法:
- 对Mn4+激活的综合实验数据集的构建.
- 机器学习模型的应用,包括K-最近邻居和额外树回归器.
- 对Eu3+兴奋剂系统的模型的验证,以评估泛化能力.
主要成果:
- 精确预测激发和发射波长,仅使用元素组成.
- K-最近的邻居和额外的树木回归器显示了特定属性预测的最高准确度.
- 成功地将其通用化为Eu3+化系统,证明了其广泛的适用性.
结论:
- 数据驱动的方法可以准确地预测的光学特性,而无需复杂的描述符.
- 这种方法促进了新型发光材料的高效和可解释的发现.
- 开发的模型为以理论为基础的材料设计提供了基础.
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