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在多元化化薄膜和涂料中,以人工智能为基础预测和对相的转换分析

Yunlong Zhu¹, Junzhi Cui², Jingli Ren¹

¹School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China.

|February 24, 2026

概括

机器学习使用元素性质准确预测化物薄膜相. 像DCR和CN这样的关键描述符指导着具有稳定结构的材料的设计.

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
机器学习应用机器学习应用

背景情况:

了解化薄膜的相位形成对于材料设计至关重要.
现有模型经常面临数据不足的局限性.

研究的目的:

为了研究化物薄膜中的组成,描述物和相结构之间的关系.
使用机器学习开发用于阶段分类的准确预测模型.
确定控制相位形成的关键描述符.

主要方法:

对包含79个系统和627个记录的数据集进行分析.
利用从元素性质和热力学参数中得出的特征.
使用渐变增强模型进行阶段分类和特征重要性分析.

主要成果:

在使用8个关键特征的阶段分类中实现了0.94的平均准确性.
确定了DCR和CN作为相位结构的主导预测因素.
在更高的 DCR 和 CN 值时,显示出对 FCC 阶段形成的偏好.
导出了相位形成的明确预测表达式.

结论:

可解释机器学习有效地预测化物薄膜中的相位结构.
像DCR和CN这样的关键描述符对于指导材料设计至关重要.
拟议的数据纳入原则,以在有限的数据条件下提高模型性能.

关键词:

应用科学应用科学涂料涂料涂料涂料材料科学是一种材料科学.

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