机器学习密度函数从随机阶段近似得到的函数
Stefan Riemelmoser1,2, Carla Verdi1,3,4, Merzuk Kaltak5
1Faculty of Physics and Center for Computational Materials Science, University of Vienna, Kolingasse 14-16, A-1090 Vienna, Austria.
Journal of chemical theory and computation
|October 6, 2023
概括
机器学习模拟随机相近似 (RPA) 来创建一个更容易访问的密度函数. 这种ML-RPA方法实现了对钻石表面的高精度,扩大了计算化学能力.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 科恩-沙姆密度函数理论 (DFT) 是第一原则计算的标准.
- 像随机相近似 (RPA) 这样的更准确的方法在计算上昂贵.
- 机器学习提供了一条降低准确理论计算成本的途径.
研究的目的:
- 开发一个机器学习模型 (ML-RPA),可以近似RPA计算.
- 为了创建一个扩展标准梯度近似的非局部密度函数.
- 评估ML-RPA对材料性能和化学系统的准确性.
主要方法:
- 使用机器学习将RPA映射到纯的Kohn-Sham密度函数.
- 使用非局部密度描述符和RPA优化了培训的有效潜力.
- 在钻石,其表面和液态水上训练一个单一的ML-RPA功能.
主要成果:
- 对于钻石表面的形成能量,ML-RPA的准确性与最先进的范德瓦尔斯函数相提并论.
- 液态水的ML-RPA性能还没有超过标准梯度近似值.
- 该研究表明,ML有可能将RPA的适用性扩展到更大的系统和时间尺度.
结论:
- 机器学习可以有效地接近先进的理论方法,如RPA.
- 在材料科学中,ML-RPA对精确的计算,特别是对固体来说,显示出前景.
- 需要进一步开发ML-RPA以准确建模液体等复杂系统.
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