从分子前体通过机器学习对二维共价有机框架进行带边预测
Dayong Wang1, Haifeng Lv1, Yangyang Wan2
1Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Sciences, Key Laboratory of Materials Sciences for Energy Conversion, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), and CAS Center for Excellence in Nanoscience, University of Science and Technology of China, Hefei, Anhui 230026, China.
机器学习模型准确地预测了二维共价有机框架 (2D COF) 的带边位置. 这种方法加速了用于光催化和纳米电子的2DCOF的设计.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 二维共价有机框架 (2D COF) 对于光催化和纳米电子学至关重要.
- 准确预测它们的带边位置是必不可少的,但计算密集.
- 目前的方法面临的挑战是由于2DCOF的结构多样性和复杂性.
研究的目的:
- 开发一种有效的策略,用于预测二维COF的带边位置.
- 为了加快发现,将第一原则计算与机器学习 (ML) 结合起来.
- 建立一个可靠的工作流程来确定2D COF带对齐.
主要方法:
- 使用第一原理计算生成二维COF属性的数据集.
- 开发和训练机器学习模型来预测价值带最大值 (VBM) 和导电带最小值 (CBM).
- 对于带边位的PBE和HSE06计算结果之间建立了线性相关性.
主要成果:
- 实现了0.229 eV (VBM) 和0.247 eV (CBM) 的平方根平均误差 (RMSE),用于与PBE计算对比的ML预测.
- 证明了PBE和HSE06之间强烈的线性关系,结果具有较低的RMSE (0.089 eV为VBM,0.042 eV为CBM).
- 成功开发了2D COF带边位置的预测工作流.
结论:
- 机器学习显著加速了2D COF带边位置的预测.
- 开发的ML策略提供了准确可靠的预测.
- 这项工作有助于针对能源和电子领域的先进应用设计2D COF.
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