推进C,Si和SiC复杂系统的带结构模拟:一种机器学习驱动的密度功能紧固结合方法
Guozheng Fan1,2, Yu Jing1, Thomas Frauenheim3,4
1Jiangsu Co-Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China. yujing@njfu.edu.cn.
我们开发了一种机器学习 (ML) 工作流来优化电子带结构,使用密度功能紧密结合 (DFTB). 这种ML方法准确地复制了各种材料和几何形状的昂贵的混合功能计算.
科学领域:
- 计算材料科学科学 计算材料科学
- 量子化学 是一个量子化学.
- 机器学习应用 机器学习应用
背景情况:
- 准确预测电子带结构对于材料设计至关重要.
- 混合功能计算提供高精度,但在计算上昂贵.
- 密度功能紧密结合 (DFTB) 提供了一个更快的替代方案,但往往缺乏精度.
研究的目的:
- 开发一种机器学习 (ML) 工作流程,以提高密度功能紧密结合 (DFTB) 计算的准确性.
- 用更快的ML-DFTB方法复制计算要求高的混合功能计算的结果.
- 为各种材料系统提供准确的电子带结构预测.
主要方法:
- 开发了一种机器学习 (ML) 工作流程,以优化电子带结构.
- 该工作流使用DFTB-ML方案来训练和预测两个中心积分和现场能量的缩放参数.
- 该模型使用碳,和碳化系统进行训练,包括散装,板块和缺陷几何.
主要成果:
- 该DFTB-ML工作流准确地复制了混合功能计算的结果.
- 该模型对靠近费米能量的电子带结构表现出了特别的准确性.
- 机器学习模型表现出极好的可转移性,允许基于对较小系统的训练对较大系统进行准确的预测.
结论:
- 开发的DFTB-ML工作流提供了一个非常准确和可适应的方法来预测电子带结构.
- 这种方法显著降低了与实现混合功能级准确性相关的计算成本.
- 该方法有可能在广泛的化学环境和材料类型中进行精确的带结构预测.
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