一个连接的卷积中性网络协议,用于设计基于修改图形的二维材料
Junqing Li1, Ziyi Liu1, Zhehuan Zhao2
1State Key Laboratory of Fine Chemicals, Liaoning Key Laboratory for Catalytic Conversion of Carbon Resources, School of Chemistry, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
The journal of physical chemistry letters
|July 25, 2024
概括
我们开发了一种新的人工智能方法,用于快速选- (B-N) 编的石墨烯材料用于电子应用. 这种方法可以有效地预测材料特性,有助于设计先进的光电子设备和催化剂.
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
- 材料中的人工智能
背景情况:
- 兴奋剂对于调整材料电子性质至关重要.
- 由于巨大的化学空间,材料选的传统试错方法效率低下.
- 石墨烯 (GDY) 是一种有前途的材料,可用于各种应用.
研究的目的:
- 开发一种用于- (B-N) 合的石墨 (GDY) 的快速选方法.
- 用人工智能来预测B-N编码GDY的频段间隙.
- 在GDY中建立B-N兴奋剂的定量结构-属性关系.
主要方法:
- 一个连接的卷积神经网络 (CCNN) 用于材料选.
- 一个配对原子局部化矩阵 (PALM) 描述符被设计来表示当地的化学环境.
- 进行了归因分析,以了解兴奋剂的影响.
主要成果:
- 该CCNN模型准确地预测了B-N合并GDY的频段间隙.
- 确定了材料结构和带隙之间的定量关系.
- 在sp2杂交站点的B-N兴奋剂对带间隙扩大的影响比sp杂交站点更大.
结论:
- 开发的AI方法为材料选提供了高精度和高效率.
- 这种方法有助于合理设计用于光电子设备和催化剂的基于GDY的材料.
- 它为设计具有所需电子性质的新材料开辟了新的可能性.
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