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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.

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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.

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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.