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一种尖端的神经网络方法用于预测有缺陷的玛-石墨烯纳米带的热电效率
Jiayi Guo1,2, Chunfeng Cui1, Tao Ouyang1
1Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.
Scientific reports
|January 8, 2025
概括
这项研究使用神经网络来预测缺陷的马-石墨烯纳米带 (γ-GYNRs) 中的热电性能. 中等,均分布的缺陷在这些低维材料中增强了热电功率 (ZT).
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算化学的计算化学
背景情况:
- 热电材料将热转化为电,反之亦然.
- 优化热电性能需要精心设计材料.
- 缺陷显著影响材料性能,包括热电性能.
研究的目的:
- 预测有缺陷的马-石墨烯纳米带 (γ-GYNRs) 的热电功率 (ZT).
- 确定缺陷度和分布对ZT的影响.
- 证明机器学习模型在设计新型低维热电材料中的有效性.
主要方法:
- 使用二进制编码,卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络.
- 采用多尺度特征融合和人工特征提取注意力机制.
- 在500个γ-GYNR结构的子集上训练模型,以预测更大的数据集的ZT.
主要成果:
- 实现了高预测准确度,R2>>0.91和平均绝对误差 (MAE) 为0.050.06.
- 确定缺陷数量和分布为高ZT的关键因素.
- 发现具有中度和均分布的缺陷的γ-GYNR表现出优越的热电性能.
结论:
- 神经网络是设计像γ-GYNRs这样的低维材料的有效工具.
- 缺陷工程是增强热电特性的一种可行的策略.
- 这些发现为探索其他具有优良热电潜力的材料提供了洞察力.
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