用双极交互模型对两极性张量器进行 Δ-机器学习
Imran Chaudhry1, Mark J Bronson1, Lasse Jensen1
1Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States.
Journal of chemical theory and computation
|July 9, 2025
概括
我们开发了一个新的机器学习模型,Delta_PIM_CCSD,以有效地预测分子极化度张量. 这种方法的准确性与类似分子的DFT/B3LYP相当,但对于更广泛的应用需要更大的数据集.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 机器学习应用 机器学习应用
背景情况:
- 分子极化对于理解光物质和分子间相互作用至关重要.
- 准确和高效的方法来计算极化张量是必不可少的.
研究的目的:
- 介绍一个新型模型,Delta_PIM_CCSD,结合一个可偏化的双极相互作用模型 (PIM) 与Delta机器学习.
- 高精度和高效率预测极化张量.
主要方法:
- 使用PIM和Delta机器学习开发了Delta_PIM_CCSD模型.
- 通过对角化PIM极化张量来适应旋转对称的参考几何.
- 从QM7b数据集中的参数化模型对合集群单双 (CCSD) 极化.
主要成果:
- 对于QM7b类分子,Delta_PIM_CCSD实现了与DFT/B3LYP可比的精度,计算成本较低.
- 在基准数据修正后,QM9数据集的准确性保持不变.
- 对于比训练集更小和更有化学多样性的分子,性能下降.
结论:
- PIM和Delta机器学习的结合为预测极化张量提供了一个有前途的方法.
- 为了更广泛的适用性,需要更大,更多样化的数据集,具有高层次的理论极化性.
- 纳入原子特定的极化性可以进一步提高模型性能.
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