机器学习的动能密度与目标和特征平滑:更好的结果与更少的训练数据更好的结果
Sergei Manzhos1, Johann Lüder2,3,4, Manabu Ihara1
1School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
The Journal of chemical physics
|December 19, 2023
概括
通过使用光滑密度变量来改进动能密度 (KED) 的机器学习模型. 这种方法为密度函数理论 (DFT) 用更少的数据点产生准确的KED函数.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 动能函数 (KEFs) 对于无轨密度函数理论 (DFT) 是至关重要的.
- 使用机器学习 (ML) 学习KEF,特别是动能密度 (KED) 函数,显示出希望.
- 目前的ML方法与从电子密度描述器生成的大型,不均的数据集作斗争.
研究的目的:
- 为KED功能开发更准确和数据效率更高的ML模型.
- 为了解决KED的ML中数据分布不均所带来的挑战.
- 改进用于无轨 DFT 的 KEF 的构建.
主要方法:
- 使用高斯过程回归 (GPR) 学习KED.
- 采用平滑密度依赖变量和KED来缓解数据分布问题.
- 训练ML模型在第四阶梯度扩张和Kohn-Sham有效潜力的平滑条件上.
主要成果:
- 同时实现了对Al,Mg和Si的准确和稳定的ML动能模型.
- 与典型的DFT数据集相比,所需的数据点显著减少 (只有2000个样本).
- 在预测研究材料的能量-体积依赖性方面获得了高精度 (约为1%).
结论:
- 平滑密度变量有效地解决了KED的ML中的数据分布挑战.
- 这种方法可以创建准确的KEF,而数据要求大大减少.
- 这种方法证明了朝着更高效,更可靠的无轨道DFT计算的可行途径.
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