面向分子可转移的费米离子神经波函数
Michael Scherbela1, Leon Gerard2, Philipp Grohs3,4,5
1Faculty of Mathematics, University of Vienna, Vienna, Austria.
Nature communications
|January 3, 2024
概括
我们开发了一个新的神经网络替代品,将简单的哈特里-福克轨道映射到精确的神经网络轨道. 这种方法使波函数模型能够在多个化合物中进行预训练,显著降低电子结构计算的计算成本.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 深度神经网络与变化的蒙特卡洛方法相结合,为电子施罗丁格方程提供了准确的解决方案.
- 目前的方法需要计算昂贵,从头开始对特定系统的波函数进行优化,这阻碍了广泛采用.
研究的目的:
- 为高效和可转移的波函数建模开发一种新的神经网络替代品.
- 为了降低与解决电子施罗丁格方程相关的计算成本.
主要方法:
- 提出了一个神经网络替代品,将非相关的Hartree-Fock轨道映射到相关的神经网络轨道.
- 通过在较小的分子碎片上预训练波函数模型并将其应用于更大的化合物来证明可转移性.
- 用实验证据验证了该方法.
主要成果:
- 拟议的套件有效地学习了跨不同化合物和几何体的单个波函数.
- 成功地将预训练模型从较小的碎片转移到较大的分子.
- 实验证据支持了概括波函数模型的可能性.
结论:
- 开发的神经网络替代品显著降低了用于高精度ab-initio能量计算的计算成本.
- 在各种化合物和几何体的预训练可以导致有效的电子结构研究的基础模型.
- 这种方法为更容易访问和更快速的计算化学模拟铺平了道路.
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