人工神经网络 拟合潜在能量曲线和表面:1/R难题
Siddhuram Rana1, Uday Sankar Manoj1, Upakarasamy Lourderaj1
1School of Chemical Sciences, National Institute of Science Education and Research (NISER) Bhubaneswar, An OCC of Homi Bhabha National Institute, Khurdha, India.
Journal of computational chemistry
|September 15, 2025
概括
人工神经网络 (ANN) 可以比潜在能量表面 (PES) 更准确地适应初始计算的电子能量值. 将库伦比克核间排斥能添加到装配的电子能量中,可以得到一个准确的PES.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 机器学习在化学中的应用
背景情况:
- 波恩-奥本海默近似在分子系统中将电子和核运动分开.
- 潜在能量表面 (PES) 对于理解分子行为和反应至关重要.
- 代表 PES 的传统方法包括分析函数和插值.
研究的目的:
- 为了研究人工神经网络 (ANN) 适应 ab initio计算的电子能量值的有效性.
- 为了比较ANN在装配电子能量的精度与总潜在能量的精度.
- 开发一种准确的方法来构建潜在能量表面.
主要方法:
- 一个分子系统的电子能量值的初始计算.
- 通过人工神经网络方法来调整计算的电子能量值.
- 将准确的库伦比核间排斥能量添加到合适的电子能量中.
主要成果:
- 与潜在能量值相比,ANN可以更准确地适应初始计算的电子能量值.
- 装配的电子能量,结合精确的库伦比核内排斥,准确地重建了潜在能量表面.
- 这种方法比传统的分析函数和插值方法提供了更好的准确性.
结论:
- 人工神经网络对于准确地表示潜在能量表面的电子能量成分是非常有效的.
- 将安装在ANN中的电子能量与精确的核排斥能量相结合,为PES构建提供了一种优越的方法.
- 这种方法在计算化学中提高了初始推导的潜在能量表面的准确性.
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