对于固体的可转移的神经波函数
L Gerard1, M Scherbela1, H Sutterud2
1Faculty of Mathematics, University of Vienna, Vienna, Austria.
Nature computational science
|October 22, 2025
概括
深度学习通过在多个固态系统中优化单个神经网络来加速量子化学计算. 这种方法显著降低了模拟材料的计算成本.
科学领域:
- 量子化学 是一个量子化学.
- 计算材料科学科学 计算材料科学
- 科学中的人工智能.
背景情况:
- 基于深度学习的变量蒙特卡洛 (DL-VMC) 为许多电子的施罗丁格方程提供了高精度.
- 由于优化神经网络重量的高计算成本,DL-VMC方法面临着挑战,因为每个新系统都需要优化神经网络重量.
研究的目的:
- 将优化单个神经网络在多个系统中的方法扩展到固态材料.
- 为了降低与模拟固体相关的计算成本,这些固体涉及不同的几何形状和条件.
主要方法:
- 实现了一个单个神经网络,在各种几何形状,边界条件和超级细胞大小中进行优化,用于固态计算.
- 将预训练的神经网络从较小的 (2x2x2) 转移到较大的 (3x3x3) LiH超级细胞.
主要成果:
- 在不同的固态变异中优化单个替代品显著减少了所需的优化步骤的数量.
- 使用转移网络模拟较大的3x3x3LiH超级电池,与以前的方法相比,需要减少50倍的优化步骤.
结论:
- 在多个固态系统中优化单个神经网络是减少计算开销的可行策略.
- 这种转移学习方法显示了高效的高通量材料模拟的前景.
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