新的算法可以生成定不变的多项式和潜在能量表面适配的基本不变量
Yiping Hao1,2, Xiaoxiao Lu1, Bina Fu1,2,3
1State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, People's Republic of China.
Journal of chemical theory and computation
|January 22, 2025
概括
一个新的算法显著加速了神经网络潜在能量表面的对称多项式的生成. 这种方法具有线性时间复杂性,使得复杂的分子对称性计算对于更大的系统是可行的.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 比对称函数,如变不变的多项式 (PIP) 和基本不变 (FI) 对于在神经网络 (NN) 潜在能量表面 (PES) 配件中整合变对称至关重要.
- 创建这些对称多项式的传统算法具有因数时间复杂度 (N! ),将其应用限制在不到10个相同原子的系统中.
研究的目的:
- 开发一种用于生成对称多项式的新算法,显著提高计算效率.
- 为了使NN PES适配能够适用于更大的分子系统的高对称性需求.
主要方法:
- 基于图形连接性分析和分子变量组生成集的作用的新算法被开发出来.
- 该算法实现了与相同原子 (N) 的数量相关的线性时间复杂性.
主要成果:
- 新的算法在生成对称多项式方面表现出了显著的加速,与以前的方法相比,15个原子分子的速度提高了大约200万倍.
- 效率的提高随着分子大小和相同原子的数量而增加.
结论:
- 开发的线性时间复杂度算法克服了因数复杂度方法的局限性.
- 这一进步使得基本不变神经网络 (FI-NN) 方法对于大,高度对称的分子系统具有实用性,进步了计算化学和材料科学.
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