加速第四代机器学习潜力使用准线性缩放粒子网电荷平衡
Moritz Gubler1, Jonas A Finkler1, Moritz R Schäfer2,3
1Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland.
Journal of chemical theory and computation
|August 16, 2024
概括
我们为机器学习潜力 (MLP) 开发了一种高效的电荷平衡 (Qeq) 方法. 这种准线性缩放方法显著降低了大型系统的计算成本,使得原子模拟更快.
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:
- 机器学习潜力 (MLP) 为原子模拟提供电子结构精度,计算成本较低.
- 当前的MLP经常根据当地环境总结原子能.
- 像电荷转移这样的非局部现象在第四代MLP中需要电荷平衡 (Qeq),从而增加计算费用.
研究的目的:
- 为机器学习潜力提供高效的电荷平衡 (Qeq) 公式.
- 在大规模模拟中克服与传统Qeq方法相关的计算瓶.
- 为了能够有效地计算能导数,以计算全球结构依赖的原子电荷.
主要方法:
- 开发了一种新的Qeq配方,避免了明确的库伦矩阵元素计算.
- 实现了Qeq步骤的准线性缩放算法.
- 能够有效计算考虑Qeq衍生的原子电荷的能量导数.
主要成果:
- 实现了Qeq程序的近线性缩放计算成本.
- 显著降低了MLP中大型系统的计算瓶.
- 成功计算了包含全球结构依赖的原子电荷的能量衍生物.
结论:
- 新的Qeq配方大大提高了需要电荷平衡的MLP的效率.
- 这种方法加速了涉及非局部现象的原子模拟,例如电荷转移.
- 该方法是通用的,适用于MLP以外的各种力场.
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