通过可学习的结构描述器辅助神经网络和转移学习对氧化物的带对齐
Shin Kiyohara1,2, Yoyo Hinuma3, Fumiyasu Oba1,4
1Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan.
Journal of the American Chemical Society
|March 28, 2024
概括
机器学习可以准确地预测氧化物的半导体带对齐,使用散装和表面数据. 这种方法加快了对电子设备材料的理解和选.
科学领域:
- 材料科学
- 计算化学
- 凝聚物质物理学
背景情况:
- 半导体,绝缘体和介电物的带对齐对于设备的性能至关重要.
- 电离电位和电子亲和度决定了表面依赖的带边位置.
- 准确的确定需要复杂的实验或模拟.
研究的目的:
- 开发一种用于预测非金属氧化物带对齐的机器学习模型.
- 为了快速和系统地预测各种氧化物表面的带位置.
主要方法:
- 使用3000个氧化物表面的高通量第一原则计算数据集.
- 开发了一种基于散装结构和表面终结信息的神经网络模型.
- 扩展该模型以纳入多重效应并适用于三元氧化物.
主要成果:
- 神经网络准确地预测了松散的二元氧化物表面的带位置.
- 该模型有效地处理多个效应和转移到三元氧化物.
- 仅使用散装结构和表面终端数据实现了准确的预测.
结论:
- 机器学习提供了一种有效的方法来确定非金属氧化物中的波段对齐.
- 这种方法有助于电子应用的系统理解和材料选.
- 能够预测各种固体表面的波段对齐.
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