施罗丁格网络:施罗丁格方程的通用神经网络解答器
Yaolong Zhang1, Bin Jiang2, Hua Guo1
1Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Journal of chemical theory and computation
|January 8, 2025
概括
一个新的神经网络SchrödingerNet解决了超出Born-Oppenheimer近似 (BOA) 的完整电子核施罗丁格方程 (SE). 这种机器学习方法有效地产生精确的潜在能量表面,并包括非BOA校正.
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:
- 机器学习的进步使得使用神经网络 (NN) 和变量蒙特卡洛,可以准确地解决电子施罗丁格方程 (SE).
- 目前基于NN的方法依赖于Born-Oppenheimer近似 (BOA) 并需要为每个核配置提供广泛的培训.
研究的目的:
- 开发一个新的NN架构,SchrödingerNet,以解决完全电子核的SE.
- 为了使连续的潜在能量表面能够高效准确地产生.
- 在单一的培训过程中纳入非Born-Oppenheimer (非BOA) 校正.
主要方法:
- 提出了一个新的NN架构,SchrödingerNet.
- 定义了一个损失函数,以在整个系统中等同局部能量.
- 采用了适应对称的总波函数替代品,包括核和电子坐标.
主要成果:
- 实现了连续潜在能量表面的准确和高效生成.
- 成功结合了非BOA纠正. 成功结合了非BOA纠正.
- 通过对原子和小分子系统的基准来证明准确性和效率.
结论:
- 施罗丁格网络提供了一种高效准确的方法来解决完全电子核SE.
- 该方法克服了基于BOA的方法的局限性.
- 允许量子力学计算与机器学习的无集成.
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