费米子神经网络下有效核心潜力的性能评估:第一和第二行元素
Mengsa Wang1,2, Yuzhi Zhou3,4, Han Wang2,5
1Graduate School of China Academy of Engineering Physics, Beijing 100088, China.
The Journal of chemical physics
|May 24, 2024
概括
本研究评估神经网络变量蒙特卡洛 (FermiNet) 方法中的有效核心潜力 (ECP). 结果显示,ECP质量普遍反映,ccECP和eCEPP ECP在电子结构计算方面表现最好.
科学领域:
- 计算化学计算化学
- 量子力学就是量子力学.
- 材料科学 材料科学 材料科学
背景情况:
- 深度学习通过神经网络变量蒙特卡洛 (FermiNet) 进行了先进的电子结构计算.
- 有效的核心潜力 (ECP) 方案提高了计算效率,但需要在新方法中进行验证.
- 在FermiNet中缺乏对ECP性能的全面评估.
研究的目的:
- 在FermiNet框架内全面评估各种有效核心潜能 (ECP) 的性能.
- 评估 FermiNet-ECP 计算对第一和第二行元素的原子,光谱和分子性质的准确性.
- 通过使用FermiNet.Net,确定最适合用于准确和高效的电子结构计算的ECP.
主要方法:
- 在不同化学系统 (原子,分子,周期系统) 中对ECP进行广泛的测试.
- 使用各种ECP进行的费尔米网计算与全电子结果的比较.
- 分析原子,光谱和分子性质以基准ECP性能.
- 对相关性一致的ECP (ccECP) 和能量一致的相关电子伪电位 (eCEPP) 性能进行评估.
主要成果:
- 费尔米网通常反映了不同ECP固有的质量.
- 在经过测试的ECP中,ccECP和eCEPP的整体性能在测试中的ECP中表现出卓越的表现.
- ccECP提供了稍微更好的光谱精度和更广泛的元素覆盖.
- eCEPP提供了核心极化和形状和能量的一致性的系统处理.
结论:
- 在FermiNet计算中,ECP质量得到了准确的表示.
- 对于 FermiNet 的精确电子结构计算,建议使用 ccECP 和 eCEPP.
- 全电子计算由于相对论效应和较重元素的数值不稳定性而面临限制.
- 未来的工作应该专注于提高FermiNet的功能,可能包含相对论效应.
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